{"id":633696,"date":"2020-01-31T16:22:49","date_gmt":"2020-02-01T00:22:49","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-event&p=633696"},"modified":"2025-08-06T11:53:30","modified_gmt":"2025-08-06T18:53:30","slug":"aaai-2020","status":"publish","type":"msr-event","link":"https:\/\/www.microsoft.com\/en-us\/research\/event\/aaai-2020\/","title":{"rendered":"Microsoft at AAAI 2020"},"content":{"rendered":"\n\n

Venue:<\/strong> New York Hilton Midtown Hotel (opens in new tab)<\/span><\/a>
\n1335 Avenue of the Americas
\nNew York, NY 10019<\/p>\n

Website:<\/strong> AAAI 2020 (opens in new tab)<\/span><\/a>Opens in a new tab<\/span><\/p>\n

Microsoft is proud to be a sponsor of the Thirty-Fourth AAAI Conference on Artificial Intelligence (opens in new tab)<\/span><\/a>. Over 50 of our researchers will be at AAAI – stop by our booth to chat with our experts, see demos of our latest research and find out about career opportunities with Microsoft.<\/p>\n

Microsoft attendees<\/h3>\n

Saleema Amershi<\/a>
\nAdam Atkinson<\/a>
\nDionisia Barkalakis
\nChristopher Bennage
\nMichael Betser
\nBiswarup Bhattacharya
\nWenfeng Cheng
\nMurali Chintalapati
\nNeha Choudhary
\nYingnong Dang
\nRui Ding
\nTony Duan
\nMarcus Fontoura
\nAdam Fourney<\/a>
\nRupert Freeman<\/a>
\nMichel Galley<\/a>
\nFeng Gao
\nJianfeng Gao<\/a>
\nYeyun Gong
\nSumit Gulwani<\/a>
\nDevin Gunson
\nShi Han<\/a>
\nMicheleen Harris
\nMatthew Hausknecht<\/a>
\nEric Horvitz<\/a>
\nKori Inkpen<\/a>
\nLei Ji<\/a>
\nEce Kamar<\/a>
\nB\u00f6rje Karlsson<\/a>
\nRyan Kelly
\nSean Kuno<\/a>
\nCuiling Lan<\/a>
\nMiran Lee
\nJinchao Li<\/a>
\nZe Li
\nNut Limsopatham
\nChin-Yew Lin<\/a>
\nJian Lin
\nMing-Chih Lin
\nZijia Lin<\/a>
\nShujie Liu<\/a>
\nXiaodong Liu<\/a>
\nShuming Ma (opens in new tab)<\/span><\/a>
\nVani Mandava<\/a>
\nDaniel McDuff<\/a>
\nMeredith Morris<\/a>
\nBesmira Nushi<\/a>
\nPeder Olsen
\nDan O’Neill
\nHamid Palangi<\/a>
\nAndi Peng<\/a>
\nBaolin Peng<\/a>
\nForough Poursabzi-Sangdeh<\/a>
\nFarrukh Rahman
\nNoam Razin (opens in new tab)<\/span><\/a>
\nHannes Schulz<\/a>
\nShahin Shayandeh<\/a>
\nAmy Siebenthaler
\nJack Stokes
\nAli Vira
\nHanna Wallach<\/a>
\nGuoxin Wang<\/a>
\nShuohang Wang
\nWei Wang<\/a>
\nJenn Wortman Vaughan<\/a>
\nYingce Xia<\/a>
\nChenyan Xiong<\/a>
\nZhangwei Xu
\nQuanzeng You
\nWenjun Zeng<\/a>
\nDongmei Zhang<\/a>
\nJian Zhang
\nGuoqing Zheng<\/a>
\nMengyu Zhou<\/a>Opens in a new tab<\/span><\/p>\n

Saturday, February 8<\/h2>\n

8:30 AM\u201312:30 PM<\/h3>\n

SA3: Recent Advances in Fair Resource Allocation<\/strong>
\nRupert Freeman (opens in new tab)<\/span><\/a>,\u00a0Nisarg\u00a0Shah<\/p>\n

8:30 AM\u201310:15 AM<\/h3>\n

SA5Q: Guidelines for Human-AI Interaction<\/strong>
\nBesmira Nushi (opens in new tab)<\/span><\/a>,\u00a0Dan Weld,\u00a0Saleema Amershi (opens in new tab)<\/span><\/a>, Adam Fourney (opens in new tab)<\/span><\/a><\/p>\n

Sunday, February 9<\/h2>\n

9:30 AM\u201310:45 AM<\/h3>\n

Tech Session 1: NLP: Entity Recognition and Linking | Trianon
\nPoster 5015: Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources<\/strong>
\nQianhui\u00a0Wu,\u00a0Zijia\u00a0Lin (opens in new tab)<\/span><\/a>,\u00a0Guoxin\u00a0Wang (opens in new tab)<\/span><\/a>,\u00a0Hui Chen,\u00a0Borje\u00a0Karlsson (opens in new tab)<\/span><\/a>,\u00a0Biqing\u00a0Huang,\u00a0Chin-Yew Lin (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 3: NLP: Machine Translation | Sutton North
\nPoster 3736: Fact-Aware Sentence Split and Rephrase with Permutation Invariant Training<\/strong>
\nYinuo\u00a0Guo,\u00a0Tao Ge (opens in new tab)<\/span><\/a>,\u00a0Furu\u00a0Wei (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 3: NLP: Machine Translation | Sutton North
\nPoster 9360: Sequence Generation with Optimal-Transport-Enhanced Reinforcement Learning<\/strong>
\nLiqun\u00a0Chen,\u00a0Ke\u00a0Bai,\u00a0Chenyang\u00a0Tao,\u00a0Yizhe\u00a0Zhang (opens in new tab)<\/span><\/a>,\u00a0Guoyin\u00a0Wang,\u00a0Wenlin\u00a0Wang,\u00a0Ricardo\u00a0Henao,\u00a0Lawrence Carin Duke<\/p>\n

9:30 AM\u201310:45 AM<\/h3>\n

Tech Session 4: Vision: 3D | Gramercy
\nPoster 4022: DGCN: Dynamic Graph Convolutional Network for Efficient Multi-Person Pose Estimation<\/strong>
\nZhongwei\u00a0Qiu,\u00a0Kai\u00a0Qiu (opens in new tab)<\/span><\/a>,\u00a0Jianlong\u00a0Fu (opens in new tab)<\/span><\/a>,\u00a0Dongmei\u00a0Fu<\/p>\n

Tech Session 5: ML: Online Learning | Murray Hill
\nPoster 1687: Stochastic Online Learning with Probabilistic Graph Feedback<\/strong>
\nShuai Li, Wei Chen (opens in new tab)<\/span><\/a>, Zheng Wen, Kwong-Sak Leong<\/p>\n

11:15 AM\u201312:30 PM<\/h3>\n

Tech Session 4: Vision: Synthesis and Generation | Gramercy
\nPoster 525: Leveraging Multi-view Image Sets for Unsupervised Intrinsic Image Decomposition and Highlight Separation<\/strong>
\nRenjiao Yi, Ping Tan, Stephen Lin<\/strong><\/p>\n

Tech Session 5: ML: Neural Nets Theory, Models and Algorithms | Murray Hill
\nPoster 3914: Model Watermarking for Image Processing Networks<\/strong>
\nJie Zhang, Dongdong Chen (opens in new tab)<\/span><\/a>, Jing Liao, Han Fang, Weiming Zhang, Wenbo Zhou, Hao Cui, Nenghai Yu<\/p>\n

2:00 PM\u20133:15 PM<\/h3>\n

Tech Session 1: NLP: Relational Learning | Trianon
\nOral 1322: Improving Entity Linking by Modeling Latent Entity Type Information<\/strong>
\nShuang Chen,\u00a0Jinpeng\u00a0Wang (opens in new tab)<\/span><\/a>,\u00a0Feng Jiang,\u00a0Chin-Yew Lin (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 3: NLP: Speech, Syntax and Semantics | Sutton North
\nPoster 3057: PHASEN: A Phase-and-Harmonics-Aware Speech Enhancement Network<\/strong>
\nDacheng Yin, Chong Luo (opens in new tab)<\/span><\/a>, Zhiwei Xiong, Wenjun Zeng (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 3: NLP: Speech, Syntax and Semantics | Sutton North
\nPoster 5343: RobuTrans: A Robust Transformer based Text-to-Speech Model<\/strong>
\nNaihan Li, Yanqing Liu (opens in new tab)<\/span><\/a>, Yu Wu (opens in new tab)<\/span><\/a>, Shujie Liu (opens in new tab)<\/span><\/a>, Sheng Zhao (opens in new tab)<\/span><\/a>, Ming Liu<\/p>\n

Tech Session 4: Vision: Object Detection | Gramercy
\nPoster 561: Uncertainty-aware Multi-shot Knowledge Distillation for Image-based Object Re-identification<\/strong>
\nXin Jin, Cuiling Lan (opens in new tab)<\/span><\/a>, Wenjun Zeng (opens in new tab)<\/span><\/a>, Zhibo Chen<\/p>\n

Tech Session 4: Vision: Object Detection | Gramercy
\nPoster 5994: Shallow Feature based Dense Attention Network for Crowd Counting<\/strong>
\nYunqi Miao, Zijia Lin (opens in new tab)<\/span><\/a>, Guiguang Ding, Jungong Han<\/p>\n

Tech Session 6: Vision: Vision + Language | Nassau
\nPoster 362: Unified Vision-Language Pre-Training for Image Captioning and VQA<\/strong>
\nLuowei Zhou, Hamid Palangi (opens in new tab)<\/span><\/a>, Lei Zhang (opens in new tab)<\/span><\/a>, Houdong Hu (opens in new tab)<\/span><\/a>, Jason Corso, Jianfeng Gao (opens in new tab)<\/span><\/a><\/p>\n

3:45 PM\u20135:15 PM<\/h3>\n

Tech Session 3: NLP: Machine Comprehension and Q&A | Sutton North
\nOral 3330: Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering<\/strong> Shangwen\u00a0Lv,\u00a0Daya\u00a0Guo,\u00a0Jingjing\u00a0Xu,\u00a0Duyu\u00a0Tang (opens in new tab)<\/span><\/a>,\u00a0Nan\u00a0Duan (opens in new tab)<\/span><\/a>,\u00a0Ming Gong (opens in new tab)<\/span><\/a>,\u00a0Linjun Shou (opens in new tab)<\/span><\/a>,\u00a0Daxin Jiang (opens in new tab)<\/span><\/a>,\u00a0Guihong\u00a0Cao (opens in new tab)<\/span><\/a>,\u00a0Songlin\u00a0Hu<\/p>\n

Tech Session 3: NLP: Machine Comprehension and Q&A | Sutton North
\nOral 8941: PIQA: Reasoning about Physical Commonsense in Natural Language<\/strong>
\nYonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao (opens in new tab)<\/span><\/a>, Yejin Choi<\/p>\n

Tech Session 3: NLP: Machine Comprehension and Q&A | Sutton North
\nPoster 3657: Segment-then-Rank: Non-factoid Question Answering on Instructional Videos<\/strong>
\nKyungjae Lee, Nan Duan (opens in new tab)<\/span><\/a>, Lei Ji (opens in new tab)<\/span><\/a>, Jason Li<\/strong>, Seungwon Hwang<\/p>\n

Tech Session 3: NLP: Machine Comprehension and Q&A | Sutton North
\nPoster: 6672: Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning<\/strong>
\nYibo Sun, Duyu Tang (opens in new tab)<\/span><\/a>, Nan Duan (opens in new tab)<\/span><\/a>, Yeyun Gong<\/strong>, Xiaocheng Feng, Bing Qin, Daxin Jiang (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 2: Application: Financial\/Econ, Medical Imaging and Health | Beekman
\nPoster 3537: Graph-Driven Generative Models for Heterogeneous Multi-Task Learning<\/strong>
\nWenlin Wang, Hongteng Xu, Zhe Gan (opens in new tab)<\/span><\/a>, Bai Li, Guoyin Wang, Liqun Chen, Qian Yang, Wenqi Wang, Lawrence Carin Duke<\/p>\n

7:30 PM\u20139:30 PM | Poster Session<\/h3>\n

APP3537:\u00a0Graph-Driven Generative Models for Heterogeneous Multi-Task Learning<\/strong>
\nWenlin\u00a0Wang,\u00a0Hongteng\u00a0Xu,\u00a0Zhe\u00a0Gan (opens in new tab)<\/span><\/a>,\u00a0Bai Li,\u00a0Guoyin\u00a0Wang,\u00a0Liqun\u00a0Chen,\u00a0Qian Yang,\u00a0Wenqi\u00a0Wang,\u00a0Lawrence Carin Duke<\/p>\n

ML1687:\u00a0Stochastic Online Learning with Probabilistic Graph Feedback<\/strong>
\nShuai Li,\u00a0Wei Chen (opens in new tab)<\/span><\/a>,\u00a0Zheng Wen,\u00a0Kwong-Sak\u00a0Leong<\/p>\n

ML3914:\u00a0Model Watermarking for Image Processing Networks<\/strong>
\nJie\u00a0Zhang,\u00a0Dongdong\u00a0Chen (opens in new tab)<\/span><\/a>,\u00a0Jing Liao,\u00a0Han Fang,\u00a0Weiming\u00a0Zhang,\u00a0Wenbo\u00a0Zhou,\u00a0Hao Cui,\u00a0Nenghai\u00a0Yu<\/p>\n

NLP3057:\u00a0PHASEN: A Phase-and-Harmonics-Aware Speech Enhancement Network<\/strong>
\nDacheng\u00a0Yin,\u00a0Chong Luo (opens in new tab)<\/span><\/a>,\u00a0Zhiwei\u00a0Xiong,\u00a0Wenjun Zeng (opens in new tab)<\/span><\/a><\/p>\n

NLP3657:\u00a0Segment-then-Rank: Non-factoid Question Answering on Instructional Videos<\/strong>
\nKyungjae\u00a0Lee,\u00a0Nan\u00a0Duan (opens in new tab)<\/span><\/a>,\u00a0Lei Ji (opens in new tab)<\/span><\/a>,\u00a0Jason Li<\/b>,\u00a0Seungwon\u00a0Hwang<\/p>\n

NLP3736: Fact-Aware Sentence Split and Rephrase with Permutation Invariant Training<\/strong>
\nYinuo\u00a0Guo,\u00a0Tao Ge (opens in new tab)<\/span><\/a>,\u00a0Furu\u00a0Wei (opens in new tab)<\/span><\/a><\/p>\n

NLP5015: Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources<\/strong>
\nQianhui\u00a0Wu,\u00a0Zijia\u00a0Lin (opens in new tab)<\/span><\/a>,\u00a0Guoxin\u00a0Wang (opens in new tab)<\/span><\/a>,\u00a0Hui Chen,\u00a0Borje\u00a0Karlsson (opens in new tab)<\/span><\/a>,\u00a0Biqing\u00a0Huang,\u00a0Chin-Yew Lin (opens in new tab)<\/span><\/a><\/p>\n

NLP5343:\u00a0RobuTrans:\u00a0A\u00a0Robust Transformer based Text-to-Speech Model<\/strong>
\nNaihan\u00a0Li,\u00a0Yanqing Liu (opens in new tab)<\/span><\/a>,\u00a0Yu Wu (opens in new tab)<\/span><\/a>,\u00a0Shujie\u00a0Liu (opens in new tab)<\/span><\/a>,\u00a0Sheng Zhao (opens in new tab)<\/span><\/a>,\u00a0Ming Liu<\/p>\n

NLP6672:\u00a0Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning<\/strong>
\nYibo\u00a0Sun,\u00a0Duyu\u00a0Tang (opens in new tab)<\/span><\/a>,\u00a0Nan\u00a0Duan (opens in new tab)<\/span><\/a>,\u00a0Yeyun\u00a0Gong<\/strong>,\u00a0Xiaocheng\u00a0Feng,\u00a0Bing Qin,\u00a0Daxin Jiang (opens in new tab)<\/span><\/a><\/p>\n

NLP8941:\u00a0PIQA: Reasoning about Physical Commonsense in Natural Language<\/strong>
\nYonatan Bisk,\u00a0Rowan Zellers,\u00a0Ronan Le Bras,\u00a0Jianfeng\u00a0Gao (opens in new tab)<\/span><\/a>,\u00a0Yejin\u00a0Choi<\/p>\n

NLP9360: Sequence Generation with Optimal-Transport-Enhanced Reinforcement Learning<\/strong>
\nLiqun\u00a0Chen,\u00a0Ke\u00a0Bai,\u00a0Chenyang\u00a0Tao,\u00a0Yizhe\u00a0Zhang (opens in new tab)<\/span><\/a>,\u00a0Guoyin\u00a0Wang,\u00a0Wenlin\u00a0Wang,\u00a0Ricardo\u00a0Henao,\u00a0Lawrence Carin Duke<\/p>\n

VIS362:\u00a0Unified Vision-Language\u00a0Pre-Training for Image Captioning and VQA<\/strong>
\nLuowei\u00a0Zhou,\u00a0Hamid\u00a0Palangi (opens in new tab)<\/span><\/a>,\u00a0Lei Zhang (opens in new tab)<\/span><\/a>,\u00a0Houdong\u00a0Hu (opens in new tab)<\/span><\/a>,\u00a0Jason Corso,\u00a0Jianfeng\u00a0Gao (opens in new tab)<\/span><\/a><\/p>\n

VIS525:\u00a0Leveraging Multi-view Image Sets for Unsupervised Intrinsic Image Decomposition and Highlight Separation<\/strong>
\nRenjiao\u00a0Yi,\u00a0Ping Tan,\u00a0Stephen Lin<\/b><\/p>\n

VIS561:\u00a0Uncertainty-aware Multi-shot Knowledge Distillation for Image-based Object Re-identification<\/strong>
\nXin\u00a0Jin,\u00a0Cuiling\u00a0Lan (opens in new tab)<\/span><\/a>,\u00a0Wenjun Zeng (opens in new tab)<\/span><\/a>,\u00a0Zhibo\u00a0Chen,<\/p>\n

VIS4022: DGCN: Dynamic Graph Convolutional Network for Efficient Multi-Person Pose Estimation<\/strong>
\nZhongwei\u00a0Qiu,\u00a0Kai\u00a0Qiu (opens in new tab)<\/span><\/a>,\u00a0Jianlong\u00a0Fu (opens in new tab)<\/span><\/a>,\u00a0Dongmei\u00a0Fu<\/p>\n

VIS5994:\u00a0Shallow Feature based Dense Attention Network for Crowd Counting<\/strong>
\nYUNQI MIAO,\u00a0Zijia\u00a0Lin (opens in new tab)<\/span><\/a>,\u00a0Guiguang\u00a0Ding,\u00a0Jungong\u00a0Han<\/p>\n

Monday, February 10<\/h2>\n

9:30 AM\u201310:45 AM<\/h3>\n

Tech Session 3: NLP: Generation | Sutton North
\nOral 6824: A Dataset for Low-Resource Stylized Sequence-to-Sequence Generation<\/strong>
\nYu Wu (opens in new tab)<\/span><\/a>,\u00a0Yunli\u00a0Wang,\u00a0Shujie\u00a0Liu (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 3: NLP: Generation | Sutton North
\nPoster 6828: Complementary Auxiliary Classifiers for Label-Conditional Text Generation<\/strong>
\nYuan Li, Chunyuan Li (opens in new tab)<\/span><\/a>, Yizhe Zhang (opens in new tab)<\/span><\/a>, Xiujun Li (opens in new tab)<\/span><\/a>, Guoqing Zheng (opens in new tab)<\/span><\/a>, Lawrence Carin Duke, Jianfeng Gao (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 3: NLP: Generation | Sutton North
\nPoster 7682: Cross-Lingual Natural Language Generation via Pre-Training<\/strong>
\nZewen Chi, Li Dong (opens in new tab)<\/span><\/a>, Furu Wei (opens in new tab)<\/span><\/a>, Wenhui Wang (opens in new tab)<\/span><\/a>, Xian-Ling Mao, Heyan Huang<\/p>\n

Tech Session 6: ML: Optimization, Neural Architecture Search, and Others | Concourse A
\nPoster 2073: Light Multi-segment Activation for Model Compression<\/strong>
\nZhenhui Xu, Guolin Ke (opens in new tab)<\/span><\/a>, Jia Zhang (opens in new tab)<\/span><\/a>, Jiang Bian (opens in new tab)<\/span><\/a>, Tieyan Liu<\/p>\n

Tech Session 8: Humans and AI | Clinton
\nOral 9127: Human-Machine Collaboration for Fast Land Cover Mapping<\/strong>
\nCaleb Robinson, Anthony Ortiz, Nikolay Malkin, Blake Elias, Andi Peng (opens in new tab)<\/span><\/a>, Dan Morris (opens in new tab)<\/span><\/a>, Bistra Dilkina, Nebojsa Jojic (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 9: Games: Description Languages and NLP | Madison
\nOral 9405: Interactive Fiction Games: A Colossal Adventure<\/strong>
\nMatthew Hausknecht (opens in new tab)<\/span><\/a>, Prithviraj Ammanabrolu, Marc-Alexandre C\u00f4t\u00e9 (opens in new tab)<\/span><\/a>, Xingdi Yuan (opens in new tab)<\/span><\/a><\/p>\n

11:15 AM\u201312:30 PM<\/h3>\n

Session 3: NLP: Machine Translation | Sutton North
\nPoster 4794: Bridging the Gap between Pre-Training and Fine-Tuning for End-to-End Speech Translation<\/strong>
\nChengyi Wang, Yu Wu (opens in new tab)<\/span><\/a>, Shujie Liu (opens in new tab)<\/span><\/a>, Zhenglu Yang, Ming Zhou (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 5: ML: Neural Nets Theory, Models and Algorithms | Murray Hill
\nOral 9804: Differential Equation Units: Learning Functional Forms of Activation Functions from Data<\/strong>
\nMohamadali Torkamani, Shiv Shankar, Pedram Rooshenas, Philip Wallis<\/strong><\/p>\n

2:00 PM\u20133:15 PM<\/h3>\n

Tech Session 4: ML: RL and Multiagent RL | Regent
\nPoster 2312: Learning Agent Communication under Limited Bandwidth by Message Pruning<\/strong>
\nHangyu Mao, Zhengchao Zhang, Zhen Xiao, Zhibo Gong, Yan Ni (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 3: NLP: Machine Translation\/Vison: Language + Vision | Sutton North
\nPoster 4133: Unicoder-VL: A Universal Encoder for Vision and Language by Cross-Modal Pre-Training<\/strong>
\nGen Li, Nan Duan (opens in new tab)<\/span><\/a>, Yuejian Fang, Ming Gong (opens in new tab)<\/span><\/a>, Daxin Jiang (opens in new tab)<\/span><\/a><\/p>\n

3:45 PM\u20135:15 PM<\/h3>\n

Tech Session 3: NLP: Information Extraction, Retrieval and Text Mining | Sutton North
\nOral 6492: Simultaneously Linking Entities and Extracting Relations from Biomedical Text without Mention-Level Supervision<\/strong>
\nTrapit Bansal, Pat Verga, Neha Choudhary<\/strong>, Andrew McCallum<\/p>\n

Tech Session 3: NLP: Information Extraction, Retrieval and Text Mining | Sutton North
\nPoster 7067: Table2Analysis: Modeling and Recommendation of Common Analysis Patterns for Multi-Dimensional Data<\/strong>
\nMengyu Zhou (opens in new tab)<\/span><\/a>, Tao Wang, Pengxin Ji, Shi Han (opens in new tab)<\/span><\/a>, Dongmei Zhang (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 9: Social Networks, Influence, and Homophily | Clinton
\nOral 5649: Adaptive Greedy Versus Non-adaptive Greedy for Influence Maximization<\/strong>
\nWei Chen (opens in new tab)<\/span><\/a>, Binghui Peng, Grant Schoenebeck, Biaoshuai Tao<\/p>\n

Tech Session 9: Social Networks, Influence, and Homophily | Clinton
\nPoster 4623: Gradient Method for Continuous Influence Maximization with Budget-Saving Considerations<\/strong>
\nWei Chen (opens in new tab)<\/span><\/a>, Weizhong Zhang, Haoyu Zhao<\/p>\n

Tech Session 10: Reasoning under Uncertainty | Madison
\nPoster 3579: Reliable and Efficient Anytime Skeleton Learning<\/strong>
\nRui Ding<\/strong>, Yanzhi Liu, Jingjing Tian, Zhouyu Fu, Shi Han (opens in new tab)<\/span><\/a>, Dongmei Zhang (opens in new tab)<\/span><\/a><\/p>\n

7:20 PM\u20139:20 PM\u00a0| Poster Session<\/h3>\n

AIW4623:\u00a0Gradient Method for Continuous Influence Maximization with Budget-Saving Considerations<\/strong>
\nWei Chen (opens in new tab)<\/span><\/a>,\u00a0Weizhong\u00a0Zhang,\u00a0Haoyu\u00a0Zhao<\/p>\n

AIW7067:\u00a0Table2Analysis: Modeling and Recommendation of Common Analysis Patterns for Multi-Dimensional Data<\/strong>
\nMengyu\u00a0Zhou (opens in new tab)<\/span><\/a>,\u00a0Tao Wang,\u00a0Pengxin\u00a0Ji,\u00a0Shi Han (opens in new tab)<\/span><\/a>,\u00a0Dongmei\u00a0Zhang (opens in new tab)<\/span><\/a><\/p>\n

APP5649:\u00a0Adaptive Greedy Versus Non-adaptive\u00a0Greedy for Influence Maximization<\/strong>
\nWei Chen (opens in new tab)<\/span><\/a>,\u00a0Binghui\u00a0Peng,\u00a0Grant\u00a0Schoenebeck,\u00a0Biaoshuai\u00a0Tao<\/p>\n

HAC9127:\u00a0Human-Machine Collaboration for Fast Land Cover Mapping<\/strong>
\nCaleb Robinson,\u00a0Anthony Ortiz,\u00a0Nikolay\u00a0Malkin,\u00a0Blake Elias,\u00a0Andi Peng (opens in new tab)<\/span><\/a>,\u00a0Dan Morris (opens in new tab)<\/span><\/a>,\u00a0Bistra\u00a0Dilkina,\u00a0Nebojsa\u00a0Jojic (opens in new tab)<\/span><\/a><\/p>\n

ML2073:\u00a0Light Multi-segment Activation for Model Compression<\/strong>
\nZhenhui\u00a0Xu,\u00a0Guolin\u00a0Ke (opens in new tab)<\/span><\/a>,\u00a0Jia Zhang (opens in new tab)<\/span><\/a>,\u00a0Jiang Bian (opens in new tab)<\/span><\/a>,\u00a0Tieyan\u00a0Liu<\/p>\n

ML2312:\u00a0Learning Agent Communication under Limited Bandwidth by Message Pruning<\/strong>
\nHangyu\u00a0Mao,\u00a0Zhengchao\u00a0Zhang,\u00a0Zhen Xiao,\u00a0Zhibo\u00a0Gong,\u00a0Yan Ni (opens in new tab)<\/span><\/a><\/p>\n

ML9405:\u00a0Interactive Fiction Games: A Colossal Adventure<\/strong>
\nMatthew\u00a0Hausknecht (opens in new tab)<\/span><\/a>,\u00a0Prithviraj\u00a0Ammanabrolu,\u00a0Marc-Alexandre\u00a0C\u00f4t\u00e9 (opens in new tab)<\/span><\/a>,\u00a0Xingdi\u00a0Yuan (opens in new tab)<\/span><\/a><\/p>\n

ML9804:\u00a0Differential Equation Units: Learning Functional Forms of Activation Functions from Data<\/strong>
\nMohamadali\u00a0Torkamani,\u00a0Shiv Shankar,\u00a0Pedram\u00a0Rooshenas,\u00a0Philip Wallis<\/b><\/p>\n

NLP4794:\u00a0Bridging the Gap between Pre-Training and Fine-Tuning for End-to-End Speech Translation<\/strong>
\nChengyi\u00a0Wang,\u00a0Yu Wu (opens in new tab)<\/span><\/a>,\u00a0Shujie\u00a0Liu (opens in new tab)<\/span><\/a>,\u00a0Zhenglu\u00a0Yang,\u00a0Ming Zhou (opens in new tab)<\/span><\/a><\/p>\n

NLP6828:\u00a0Complementary Auxiliary Classifiers for Label-Conditional Text Generation<\/strong>
\nYuan Li,\u00a0Chunyuan\u00a0Li (opens in new tab)<\/span><\/a>,\u00a0Yizhe\u00a0Zhang (opens in new tab)<\/span><\/a>,\u00a0Xiujun\u00a0Li (opens in new tab)<\/span><\/a>,\u00a0Guoqing\u00a0Zheng (opens in new tab)<\/span><\/a>,\u00a0Lawrence Carin Duke,\u00a0Jianfeng\u00a0Gao (opens in new tab)<\/span><\/a><\/p>\n

NLP7682:\u00a0Cross-Lingual Natural Language Generation via Pre-Training<\/strong>
\nZewen\u00a0Chi,\u00a0Li Dong (opens in new tab)<\/span><\/a>,\u00a0Furu\u00a0Wei (opens in new tab)<\/span><\/a>,\u00a0Wenhui\u00a0Wang (opens in new tab)<\/span><\/a>,\u00a0Xian-Ling Mao,\u00a0Heyan\u00a0Huang<\/p>\n

RU3579:\u00a0Reliable and Efficient Anytime Skeleton Learning<\/strong>
\nRui Ding<\/strong>,\u00a0Yanzhi\u00a0Liu,\u00a0Jingjing\u00a0Tian,\u00a0Zhouyu\u00a0Fu,\u00a0Shi Han (opens in new tab)<\/span><\/a>,\u00a0Dongmei\u00a0Zhang (opens in new tab)<\/span><\/a><\/p>\n

VIS4133:\u00a0Unicoder-VL: A Universal Encoder for Vision and Language by Cross-Modal Pre-Training<\/strong>
\nGen Li,\u00a0Nan\u00a0Duan (opens in new tab)<\/span><\/a>,\u00a0Yuejian\u00a0Fang,\u00a0Ming Gong (opens in new tab)<\/span><\/a>,\u00a0Daxin Jiang (opens in new tab)<\/span><\/a><\/p>\n

Tuesday, February 11<\/h2>\n

9:30 AM\u201310:45 AM<\/h3>\n

Tech Session 4: ML: RL and Multiagent RL | Regent
\nPoster 6906: Metareasoning in Modular Software Systems: On-the-Fly Configuration Using Reinforcement Learning with Rich Contextual Representations<\/strong>
\nAditya Modi, Debadeepta Dey (opens in new tab)<\/span><\/a>, Alekh Agarwal (opens in new tab)<\/span><\/a>, Adith Swaminathan (opens in new tab)<\/span><\/a>, Besmira Nushi (opens in new tab)<\/span><\/a>, Sean Andrist (opens in new tab)<\/span><\/a>, Eric Horvitz (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 4: ML: RL and Multiagent RL | Regent
\nPoster 9280: Actor Critic Deep Reinforcement Learning for Neural Malware Control<\/strong>
\nYu Wang<\/strong>, Jack Stokes<\/strong>, Mady Marinescu<\/strong><\/p>\n

Tech Session 5: Vision: Image Retrieval, Ranking, Recognition | Gramercy
\nPoster 7144: Functionality Discovery and Prediction of Physical Objects<\/strong>
\nLei Ji (opens in new tab)<\/span><\/a>, Botian Shi, Xianglin Guo, Xilin Chen<\/p>\n

11:15 AM\u201312:30 PM<\/h3>\n

Tech Session 3: NLP: Representation Learning | Sutton South
\nOral 3656: P-SIF: Document Embeddings Using Partition Averaging<\/strong>
\nVivek Gupta, Ankit Saw, Pegah Nokhiz, Praneeth Netrapalli (opens in new tab)<\/span><\/a>, Piyush Rai, Partha Talukdar<\/p>\n

Tech Session 3: NLP: Representation Learning | Sutton South
\nOral 5766: TextNAS: A Neural Architecture Search Space Tailored for Text Representation<\/strong>
\nYujing Wang, Yaming Yang, Yiren Chen, Jing Bai (opens in new tab)<\/span><\/a>, Ce Zhang, Guinan Su, Xiaoyu Kou, Yunhai Tong, Mao Yang (opens in new tab)<\/span><\/a>, Lidong Zhou (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 3: NLP: Representation Learning | Sutton South
\nPoster 7202: Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding<\/strong>
\nOren Barkan, Noam Razin (opens in new tab)<\/span><\/a>, Itzik Malkiel (opens in new tab)<\/span><\/a>, Ori Katz<\/strong>, Avi Caciularu (opens in new tab)<\/span><\/a>, Noam Koenigstein (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 4: ML: Neural Nets Theory, Models and Algorithms | Regent
\nPoster 1536: Posterior-Guided Neural Architecture Search<\/strong>
\nYizhou Zhou, Xiaoyan Sun (opens in new tab)<\/span><\/a>, Chong Luo (opens in new tab)<\/span><\/a>, Zheng-Jun Zha, Wenjun Zeng (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 12: Mechanism Design and Fair Allocation | Morgan
\nOral 4617: Online Second Price Auction with Semi-bandit Feedback Under the Non-Stationary Setting<\/strong>
\nHaoyu Zhao, Wei Chen<\/p>\n

2:00 PM\u20133:30 PM<\/h3>\n

Tech Session 2: NLP: Representation Learning, Summarization | Sutton North
\nPoster 9252: Latent Relation Language Models<\/strong>
\nHiroaki Hayashi, Zecong Hu, Chenyan Xiong (opens in new tab)<\/span><\/a>, Graham Neubig<\/p>\n

Tech Session 3: NLP: Semantics and Summarization | Sutton South
\nOral 3184: Graph-based Transformer with Cross-Candidate Verification for Semantic Parsing<\/strong>
\nBo Shao, Yeyun Gong<\/strong>, Weizhen Qi, Guihong Cao (opens in new tab)<\/span><\/a>, Jianshu Ji (opens in new tab)<\/span><\/a>, Xiaola Lin<\/p>\n

Tech Session 5: Vision: Human: Pose and Motion Analysis, Detection, Modeling | Gramercy
\nPoster 2669: Semantics-Aligned Representation Learning for Person Re-identification<\/strong>
\nXin Jin, Cuiling Lan (opens in new tab)<\/span><\/a>, Wenjun Zeng (opens in new tab)<\/span><\/a>, Guoqiang Wei, Zhibo Chen<\/p>\n

Tech Session 5: Vision: Human: Pose and Motion Analysis, Detection, Modeling | Gramercy
\nPoster 8881: Crowd Counting with Decomposed Uncertainty<\/strong>
\nMin-hwan Oh, Peder Olsen (opens in new tab)<\/span><\/a>, Karthikeyan Natesan Ramamurthy<\/p>\n

Tech Session 7: Vision: Vision + Language | Nassau
\nPoster 1374: Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language<\/strong>
\nSongyang Zhang, Houwen Peng (opens in new tab)<\/span><\/a>, Jianlong Fu (opens in new tab)<\/span><\/a>, Jiebo Luo<\/p>\n

Tech Session 7: Vision: Vision + Language | Nassau
\nPoster 2236: DualVD: An Adaptive Dual Encoding Model for Deep Visual Understanding in Visual Dialogue<\/strong>
\nXiaoze Jiang, Jing Yu, Yingying Zhuang, Zengchang Qin, Yue Hu, Qi Wu, Xingxing Zhang (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 7: Vision: Vision + Language | Nassau
\nPoster 3499: What Makes A Good Story? Designing Composite Rewards for Visual Storytelling<\/strong>
\nJunjie Hu, Yu Cheng (opens in new tab)<\/span><\/a>, Zhe Gan (opens in new tab)<\/span><\/a>, Jingjing Liu (opens in new tab)<\/span><\/a>, Jianfeng Gao (opens in new tab)<\/span><\/a>, Graham Neubig,<\/p>\n

Tech Session 13: Mechanism Design | Bryant
\nOral 1796: Preventing Arbitrage from Collusion When Eliciting Probabilities<\/strong>
\nRupert Freeman (opens in new tab)<\/span><\/a>, David Pennock, Dominik Peters, Bo Waggoner<\/p>\n

Wednesday, February 12<\/h2>\n

11:30 AM\u201312:30 PM<\/h3>\n

Tech Session 1: NLP: Language Models | Trianon
\nOral 4504: Alternating Language Modeling for Cross-Lingual Pre-Training<\/strong>
\nJian Yang,\u00a0Shuming\u00a0Ma (opens in new tab)<\/span><\/a>,\u00a0Dongdong\u00a0Zhang (opens in new tab)<\/span><\/a>,\u00a0ShuangZhi\u00a0Wu,\u00a0Zhoujun\u00a0Li,\u00a0Ming Zhou (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 4: NLP: Semantics and Summarization | Sutton South
\nOral 7131: Multi-level Head-wise Match and Aggregation in Transformer for Textual Sequence Matching<\/strong>
\nShuohang\u00a0Wang (opens in new tab)<\/span><\/a>,\u00a0Yunshi\u00a0Lan,\u00a0Yi Tay,\u00a0Jing Jiang,\u00a0Jingjing\u00a0Liu (opens in new tab)<\/span><\/a><\/p>\n

2:00 PM\u20133:20 PM<\/h3>\n

Tech Session 1: NLP: Machine Translation, Entity Recognition and Linking | Trianon
\nOral 2729: Fine-Tuning by Curriculum Learning for Non-Autoregressive Neural Machine Translation<\/strong>
\nJunliang Guo, Xu Tan (opens in new tab)<\/span><\/a>, Linli Xu, Tao Qin (opens in new tab)<\/span><\/a>, Tieyan Liu, Enhong Chen<\/p>\n

Tech Session 1: NLP: Machine Translation, Entity Recognition and Linking | Trianon
\nOral 3465: Transductive Ensemble Learning for Neural Machine Translation<\/strong>
\nYiren Wang, Lijun Wu, Yingce Xia (opens in new tab)<\/span><\/a>, Tao Qin (opens in new tab)<\/span><\/a>, Cheng Xiang Zhai, Tieyan Liu<\/p>\n

6:30 PM\u20138:30 PM | Poster Session<\/h3>\n

AIW7144:\u00a0Functionality Discovery and Prediction of Physical Objects<\/strong>
\nLei Ji (opens in new tab)<\/span><\/a>,\u00a0Botian\u00a0Shi,\u00a0Xianglin\u00a0Guo,\u00a0Xilin\u00a0Chen<\/p>\n

APP9280:\u00a0Actor Critic Deep Reinforcement Learning for Neural Malware Control<\/strong>
\nYu Wang<\/b>,<\/b>\u00a0Jack Stokes<\/b>,<\/b>\u00a0Mady<\/b>\u00a0Marinescu<\/b><\/p>\n

GTEP1796:\u00a0Preventing Arbitrage\u00a0from Collusion When Eliciting Probabilities<\/strong>
\nRupert Freeman (opens in new tab)<\/span><\/a>,\u00a0David Pennock,\u00a0Dominik Peters,\u00a0Bo Waggoner<\/p>\n

ML1536:\u00a0Posterior-Guided Neural Architecture Search<\/strong>
\nYizhou\u00a0Zhou,\u00a0Xiaoyan\u00a0Sun (opens in new tab)<\/span><\/a>,\u00a0Chong Luo (opens in new tab)<\/span><\/a>,\u00a0Zheng-Jun\u00a0Zha,\u00a0Wenjun Zeng (opens in new tab)<\/span><\/a><\/p>\n

ML2669:\u00a0Semantics-Aligned Representation Learning for Person Re-identification<\/strong>
\nXin\u00a0Jin,\u00a0Cuiling\u00a0Lan (opens in new tab)<\/span><\/a>,\u00a0Wenjun Zeng (opens in new tab)<\/span><\/a>,\u00a0Guoqiang\u00a0Wei,\u00a0Zhibo\u00a0Chen<\/p>\n

ML3465:\u00a0Transductive\u00a0Ensemble Learning for Neural Machine Translation<\/strong>
\nYiren\u00a0Wang,\u00a0Lijun Wu,\u00a0Yingce\u00a0Xia (opens in new tab)<\/span><\/a>,\u00a0Tao Qin (opens in new tab)<\/span><\/a>,\u00a0Cheng Xiang\u00a0Zhai,\u00a0Tieyan\u00a0Liu<\/p>\n

ML4617:\u00a0Online Second Price Auction with Semi-bandit Feedback Under the Non-Stationary Setting<\/strong>
\nHaoyu\u00a0Zhao,\u00a0Wei Chen (opens in new tab)<\/span><\/a><\/p>\n

ML6906:\u00a0Metareasoning\u00a0in Modular Software Systems: On-the-Fly Configuration Using Reinforcement Learning with Rich Contextual Representations<\/strong>
\nAditya Modi,\u00a0Debadeepta\u00a0Dey (opens in new tab)<\/span><\/a>,\u00a0Alekh\u00a0Agarwal (opens in new tab)<\/span><\/a>,\u00a0Adith\u00a0Swaminathan (opens in new tab)<\/span><\/a>,\u00a0Besmira Nushi (opens in new tab)<\/span><\/a>,\u00a0Sean Andrist (opens in new tab)<\/span><\/a>,\u00a0Eric Horvitz (opens in new tab)<\/span><\/a><\/p>\n

NLP2729:\u00a0Fine-Tuning by Curriculum Learning for Non-Autoregressive Neural Machine Translation<\/strong>
\nJunliang\u00a0Guo,\u00a0Xu Tan (opens in new tab)<\/span><\/a>,\u00a0Linli\u00a0Xu,\u00a0Tao Qin (opens in new tab)<\/span><\/a>,\u00a0Tieyan\u00a0Liu,\u00a0Enhong\u00a0Chen<\/p>\n

NLP3184:\u00a0Graph-based Transformer with Cross-Candidate Verification for Semantic Parsing<\/strong>
\nBo Shao,\u00a0Yeyun<\/b>\u00a0Gong<\/b>,\u00a0Weizhen\u00a0Qi,\u00a0Guihong\u00a0Cao (opens in new tab)<\/span><\/a>,\u00a0Jianshu\u00a0Ji (opens in new tab)<\/span><\/a>,\u00a0Xiaola\u00a0Lin<\/p>\n

NLP3656:\u00a0P-SIF: Document Embeddings Using Partition Averaging<\/strong>
\nVivek Gupta,\u00a0Ankit Saw,\u00a0Pegah\u00a0Nokhiz,\u00a0Praneeth\u00a0Netrapalli (opens in new tab)<\/span><\/a>,\u00a0Piyush Rai,\u00a0Partha\u00a0Talukdar<\/p>\n

NLP5766:\u00a0TextNAS: A Neural Architecture Search Space\u00a0Tailored for Text Representation<\/strong>
\nYujing\u00a0Wang,\u00a0Yaming\u00a0Yang,\u00a0Yiren\u00a0Chen,\u00a0Jing Bai (opens in new tab)<\/span><\/a>,\u00a0Ce Zhang,\u00a0Guinan\u00a0Su,\u00a0Xiaoyu\u00a0Kou,\u00a0Yunhai\u00a0Tong,\u00a0Mao Yang (opens in new tab)<\/span><\/a>,\u00a0Lidong\u00a0Zhou (opens in new tab)<\/span><\/a><\/p>\n

NLP7202:\u00a0Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding<\/strong>
\nOren\u00a0Barkan,\u00a0Noam\u00a0Razin (opens in new tab)<\/span><\/a>,\u00a0Itzik\u00a0Malkiel (opens in new tab)<\/span><\/a>,\u00a0Ori Katz<\/strong>,\u00a0Avi\u00a0Caciularu (opens in new tab)<\/span><\/a>,\u00a0Noam\u00a0Koenigstein (opens in new tab)<\/span><\/a><\/p>\n

NLP9252:\u00a0Latent Relation Language Models<\/strong>
\nHiroaki Hayashi,\u00a0Zecong\u00a0Hu,\u00a0Chenyan\u00a0Xiong (opens in new tab)<\/span><\/a>,\u00a0Graham\u00a0Neubig<\/p>\n

VIS1374:\u00a0Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language<\/strong>
\nSongyang\u00a0Zhang,\u00a0Houwen\u00a0Peng (opens in new tab)<\/span><\/a>,\u00a0Jianlong\u00a0Fu (opens in new tab)<\/span><\/a>,\u00a0Jiebo\u00a0Luo<\/p>\n

VIS2236:\u00a0DualVD: An Adaptive Dual Encoding Model for Deep Visual Understanding in Visual Dialogue<\/strong>
\nXiaoze\u00a0Jiang,\u00a0Jing Yu,\u00a0Yingying Zhuang,\u00a0Zengchang\u00a0Qin,\u00a0Yue Hu,\u00a0Qi Wu,\u00a0Xingxing\u00a0Zhang (opens in new tab)<\/span><\/a><\/p>\n

VIS3499:\u00a0What Makes A Good Story? Designing Composite Rewards for Visual Storytelling<\/strong>
\nJunjie\u00a0Hu,\u00a0Yu Cheng (opens in new tab)<\/span><\/a>,\u00a0Zhe\u00a0Gan (opens in new tab)<\/span><\/a>,\u00a0Jingjing\u00a0Liu (opens in new tab)<\/span><\/a>,\u00a0Jianfeng\u00a0Gao (opens in new tab)<\/span><\/a>,\u00a0Graham\u00a0Neubig,<\/p>\n

VIS8881:\u00a0Crowd Counting with Decomposed Uncertainty<\/strong>
\nMin-hwan\u00a0Oh,\u00a0Peder\u00a0Olsen (opens in new tab)<\/span><\/a>,\u00a0Karthikeyan Natesan RamamurthyOpens in a new tab<\/span><\/p>\n

Friday, February 7<\/h2>\n

W1: Affective Content Analysis (AffCon\u00a02020): Interactive Affective Response (opens in new tab)<\/span><\/a>
\nInvited Speaker:\u00a0Daniel McDuff (opens in new tab)<\/span><\/a><\/p>\n

W5:\u00a0Artificial Intelligence of Things (AIoT) (opens in new tab)<\/span><\/a>
\nProgram Co-Chairs:\u00a0Jian Zhang<\/b>
\nSteering Committee Member:\u00a0Victor\u00a0Bahl (opens in new tab)<\/span><\/a>
\nKeynote Speaker:\u00a0Ranveer Chandra (opens in new tab)<\/span><\/a>
\nAccepted Paper: In-Car Cognition with Edge Artificial Intelligence Accelerators
\nMing-<\/b>Chih<\/b>\u00a0Lin,\u00a0<\/b>Sio<\/b>-Sun Wong, Yu-<\/b>Kwen<\/b>\u00a0Hsu,\u00a0<\/b>Ti<\/b>-Hua Yang, Pi-Sui Hsu, He-Wei Lee, Wei-Chen Tsai, Hsiu-Tzu Wu, Sung-Lin Yeh<\/b><\/p>\n

W7:\u00a0Cloud Intelligence: AI\/ML for Efficient and Manageable Cloud Services (opens in new tab)<\/span><\/a>
\nProgram Chair:\u00a0Jian Zhang<\/b>
\nSteering Committee Members:Ricardo\u00a0Bianchini (opens in new tab)<\/span><\/a>, Marcus\u00a0Fontoura (opens in new tab)<\/span><\/a>,\u00a0Dongmei\u00a0Zhang (opens in new tab)<\/span><\/a>
\nKeynote Speaker:\u00a0Marcus\u00a0Fontoura (opens in new tab)<\/span><\/a>
\nInvited Speaker:\u00a0Dongmei\u00a0Zhang (opens in new tab)<\/span><\/a><\/p>\n

W14: Workshop on Intelligent Process Automation (opens in new tab)<\/span><\/a>
\nKeynote Speaker: Sumit Gulwani (opens in new tab)<\/span><\/a><\/p>\n

W22: Reproducibility in AI (RAI 2020) \u2014 Future Direction and Reproducibility Challenge (opens in new tab)<\/span><\/a>
\nInvited Speaker: Vani Mandava (opens in new tab)<\/span><\/a><\/p>\n

Saturday, February 8<\/h2>\n

W9:\u00a0The Eighth Dialog System Technology Challenge (DSTC8) (opens in new tab)<\/span><\/a>
\nWorkshop Chair:\u00a0Michel Galley (opens in new tab)<\/span><\/a>
\nTrack 1 Co-organizers: Jinchao Li (opens in new tab)<\/span><\/a>, Baolin Peng (opens in new tab)<\/span><\/a>, Jianfeng Gao (opens in new tab)<\/span><\/a>, Hannes Schulz (opens in new tab)<\/span><\/a>, Adam Atkinson (opens in new tab)<\/span><\/a>, Mahmoud Adada (opens in new tab)<\/span><\/a><\/p>\n

W11: Evaluating Evaluation of AI Systems (opens in new tab)<\/span><\/a>
\nProgram Committee Members:\u00a0Sid Suri (opens in new tab)<\/span><\/a>, Omar Alonso (opens in new tab)<\/span><\/a>Opens in a new tab<\/span><\/p>\n

Azure Global<\/h3>\n

Research Intern (opens in new tab)<\/span><\/a>
\nSenior Software Engineer (opens in new tab)<\/span><\/a>
\nPrincipal SDE (opens in new tab)<\/span><\/a><\/p>\n

Azure Production Infrastructure Engineering Team<\/h3>\n

Senior Software Engineer (opens in new tab)<\/span><\/a>
\nData and Applied Scientist II (opens in new tab)<\/span><\/a><\/p>\n

Bing<\/h3>\n

Senior Applied Scientist (opens in new tab)<\/span><\/a>
\nSenior Applied Scientist (opens in new tab)<\/span><\/a><\/p>\n

Security and Compliance Loonshot Research Team<\/h3>\n

Data Engineer (opens in new tab)<\/span><\/a>
\nSenior Security Engineer (opens in new tab)<\/span><\/a>
\nSenior Research Program Manager (opens in new tab)<\/span><\/a>
\nPrincipal Applied Researcher (opens in new tab)<\/span><\/a>
\nPrincipal Applied Researcher (opens in new tab)<\/span><\/a>
\nApplied Researcher II (opens in new tab)<\/span><\/a>
\nApplied Researcher II (opens in new tab)<\/span><\/a><\/p>\n

Full-Time Opportunities for PhD Students or Recent Graduates<\/h3>\n

AI Researcher (opens in new tab)<\/span><\/a>
\nComputer Vision Engineer (opens in new tab)<\/span><\/a>
\nNatural Language Processing Software Development Engineer (opens in new tab)<\/span><\/a>
\nMachine Learning Engineer (opens in new tab)<\/span><\/a>
\nProgram Manager & Software Engineer (opens in new tab)<\/span><\/a>Opens in a new tab<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Microsoft is proud to be a sponsor of the Thirty-Fourth AAAI Conference on Artificial Intelligence. Over 50 of our researchers will be at AAAI – stop by our booth to chat with our experts, see demos of our latest research and find out about career opportunities with Microsoft.<\/p>\n","protected":false},"featured_media":634065,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr_startdate":"2020-02-07","msr_enddate":"2020-02-12","msr_location":"New York, NY","msr_expirationdate":"","msr_event_recording_link":"","msr_event_link":"","msr_event_link_redirect":false,"msr_event_time":"","msr_hide_region":true,"msr_private_event":false,"msr_hide_image_in_river":0,"footnotes":""},"research-area":[13556],"msr-region":[197900],"msr-event-type":[197941],"msr-video-type":[],"msr-locale":[268875],"msr-program-audience":[],"msr-post-option":[],"msr-impact-theme":[],"class_list":["post-633696","msr-event","type-msr-event","status-publish","has-post-thumbnail","hentry","msr-research-area-artificial-intelligence","msr-region-north-america","msr-event-type-conferences","msr-locale-en_us"],"msr_about":"\n\n

Venue:<\/strong> New York Hilton Midtown Hotel (opens in new tab)<\/span><\/a>
\n1335 Avenue of the Americas
\nNew York, NY 10019<\/p>\n

Website:<\/strong> AAAI 2020 (opens in new tab)<\/span><\/a>Opens in a new tab<\/span><\/p>\n

Microsoft is proud to be a sponsor of the Thirty-Fourth AAAI Conference on Artificial Intelligence<\/a>. Over 50 of our researchers will be at AAAI – stop by our booth to chat with our experts, see demos of our latest research and find out about career opportunities with Microsoft.<\/p>\n

Microsoft attendees<\/h3>\n

Saleema Amershi<\/a>
\nAdam Atkinson<\/a>
\nDionisia Barkalakis
\nChristopher Bennage
\nMichael Betser
\nBiswarup Bhattacharya
\nWenfeng Cheng
\nMurali Chintalapati
\nNeha Choudhary
\nYingnong Dang
\nRui Ding
\nTony Duan
\nMarcus Fontoura
\nAdam Fourney<\/a>
\nRupert Freeman<\/a>
\nMichel Galley<\/a>
\nFeng Gao
\nJianfeng Gao<\/a>
\nYeyun Gong
\nSumit Gulwani<\/a>
\nDevin Gunson
\nShi Han<\/a>
\nMicheleen Harris
\nMatthew Hausknecht<\/a>
\nEric Horvitz<\/a>
\nKori Inkpen<\/a>
\nLei Ji<\/a>
\nEce Kamar<\/a>
\nB\u00f6rje Karlsson<\/a>
\nRyan Kelly
\nSean Kuno<\/a>
\nCuiling Lan<\/a>
\nMiran Lee
\nJinchao Li<\/a>
\nZe Li
\nNut Limsopatham
\nChin-Yew Lin<\/a>
\nJian Lin
\nMing-Chih Lin
\nZijia Lin<\/a>
\nShujie Liu<\/a>
\nXiaodong Liu<\/a>
\nShuming Ma<\/a>
\nVani Mandava<\/a>
\nDaniel McDuff<\/a>
\nMeredith Morris<\/a>
\nBesmira Nushi<\/a>
\nPeder Olsen
\nDan O’Neill
\nHamid Palangi<\/a>
\nAndi Peng<\/a>
\nBaolin Peng<\/a>
\nForough Poursabzi-Sangdeh<\/a>
\nFarrukh Rahman
\nNoam Razin<\/a>
\nHannes Schulz<\/a>
\nShahin Shayandeh<\/a>
\nAmy Siebenthaler
\nJack Stokes
\nAli Vira
\nHanna Wallach<\/a>
\nGuoxin Wang<\/a>
\nShuohang Wang
\nWei Wang<\/a>
\nJenn Wortman Vaughan<\/a>
\nYingce Xia<\/a>
\nChenyan Xiong<\/a>
\nZhangwei Xu
\nQuanzeng You
\nWenjun Zeng<\/a>
\nDongmei Zhang<\/a>
\nJian Zhang
\nGuoqing Zheng<\/a>
\nMengyu Zhou<\/a>Opens in a new tab<\/span><\/p>\n

Saturday, February 8<\/h2>\n

8:30 AM\u201312:30 PM<\/h3>\n

SA3: Recent Advances in Fair Resource Allocation<\/strong>
\nRupert Freeman (opens in new tab)<\/span><\/a>,\u00a0Nisarg\u00a0Shah<\/p>\n

8:30 AM\u201310:15 AM<\/h3>\n

SA5Q: Guidelines for Human-AI Interaction<\/strong>
\nBesmira Nushi (opens in new tab)<\/span><\/a>,\u00a0Dan Weld,\u00a0Saleema Amershi (opens in new tab)<\/span><\/a>, Adam Fourney (opens in new tab)<\/span><\/a><\/p>\n

Sunday, February 9<\/h2>\n

9:30 AM\u201310:45 AM<\/h3>\n

Tech Session 1: NLP: Entity Recognition and Linking | Trianon
\nPoster 5015: Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources<\/strong>
\nQianhui\u00a0Wu,\u00a0Zijia\u00a0Lin (opens in new tab)<\/span><\/a>,\u00a0Guoxin\u00a0Wang (opens in new tab)<\/span><\/a>,\u00a0Hui Chen,\u00a0Borje\u00a0Karlsson (opens in new tab)<\/span><\/a>,\u00a0Biqing\u00a0Huang,\u00a0Chin-Yew Lin (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 3: NLP: Machine Translation | Sutton North
\nPoster 3736: Fact-Aware Sentence Split and Rephrase with Permutation Invariant Training<\/strong>
\nYinuo\u00a0Guo,\u00a0Tao Ge (opens in new tab)<\/span><\/a>,\u00a0Furu\u00a0Wei (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 3: NLP: Machine Translation | Sutton North
\nPoster 9360: Sequence Generation with Optimal-Transport-Enhanced Reinforcement Learning<\/strong>
\nLiqun\u00a0Chen,\u00a0Ke\u00a0Bai,\u00a0Chenyang\u00a0Tao,\u00a0Yizhe\u00a0Zhang (opens in new tab)<\/span><\/a>,\u00a0Guoyin\u00a0Wang,\u00a0Wenlin\u00a0Wang,\u00a0Ricardo\u00a0Henao,\u00a0Lawrence Carin Duke<\/p>\n

9:30 AM\u201310:45 AM<\/h3>\n

Tech Session 4: Vision: 3D | Gramercy
\nPoster 4022: DGCN: Dynamic Graph Convolutional Network for Efficient Multi-Person Pose Estimation<\/strong>
\nZhongwei\u00a0Qiu,\u00a0Kai\u00a0Qiu (opens in new tab)<\/span><\/a>,\u00a0Jianlong\u00a0Fu (opens in new tab)<\/span><\/a>,\u00a0Dongmei\u00a0Fu<\/p>\n

Tech Session 5: ML: Online Learning | Murray Hill
\nPoster 1687: Stochastic Online Learning with Probabilistic Graph Feedback<\/strong>
\nShuai Li, Wei Chen (opens in new tab)<\/span><\/a>, Zheng Wen, Kwong-Sak Leong<\/p>\n

11:15 AM\u201312:30 PM<\/h3>\n

Tech Session 4: Vision: Synthesis and Generation | Gramercy
\nPoster 525: Leveraging Multi-view Image Sets for Unsupervised Intrinsic Image Decomposition and Highlight Separation<\/strong>
\nRenjiao Yi, Ping Tan, Stephen Lin<\/strong><\/p>\n

Tech Session 5: ML: Neural Nets Theory, Models and Algorithms | Murray Hill
\nPoster 3914: Model Watermarking for Image Processing Networks<\/strong>
\nJie Zhang, Dongdong Chen (opens in new tab)<\/span><\/a>, Jing Liao, Han Fang, Weiming Zhang, Wenbo Zhou, Hao Cui, Nenghai Yu<\/p>\n

2:00 PM\u20133:15 PM<\/h3>\n

Tech Session 1: NLP: Relational Learning | Trianon
\nOral 1322: Improving Entity Linking by Modeling Latent Entity Type Information<\/strong>
\nShuang Chen,\u00a0Jinpeng\u00a0Wang (opens in new tab)<\/span><\/a>,\u00a0Feng Jiang,\u00a0Chin-Yew Lin (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 3: NLP: Speech, Syntax and Semantics | Sutton North
\nPoster 3057: PHASEN: A Phase-and-Harmonics-Aware Speech Enhancement Network<\/strong>
\nDacheng Yin, Chong Luo (opens in new tab)<\/span><\/a>, Zhiwei Xiong, Wenjun Zeng (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 3: NLP: Speech, Syntax and Semantics | Sutton North
\nPoster 5343: RobuTrans: A Robust Transformer based Text-to-Speech Model<\/strong>
\nNaihan Li, Yanqing Liu (opens in new tab)<\/span><\/a>, Yu Wu (opens in new tab)<\/span><\/a>, Shujie Liu (opens in new tab)<\/span><\/a>, Sheng Zhao (opens in new tab)<\/span><\/a>, Ming Liu<\/p>\n

Tech Session 4: Vision: Object Detection | Gramercy
\nPoster 561: Uncertainty-aware Multi-shot Knowledge Distillation for Image-based Object Re-identification<\/strong>
\nXin Jin, Cuiling Lan (opens in new tab)<\/span><\/a>, Wenjun Zeng (opens in new tab)<\/span><\/a>, Zhibo Chen<\/p>\n

Tech Session 4: Vision: Object Detection | Gramercy
\nPoster 5994: Shallow Feature based Dense Attention Network for Crowd Counting<\/strong>
\nYunqi Miao, Zijia Lin (opens in new tab)<\/span><\/a>, Guiguang Ding, Jungong Han<\/p>\n

Tech Session 6: Vision: Vision + Language | Nassau
\nPoster 362: Unified Vision-Language Pre-Training for Image Captioning and VQA<\/strong>
\nLuowei Zhou, Hamid Palangi (opens in new tab)<\/span><\/a>, Lei Zhang (opens in new tab)<\/span><\/a>, Houdong Hu (opens in new tab)<\/span><\/a>, Jason Corso, Jianfeng Gao (opens in new tab)<\/span><\/a><\/p>\n

3:45 PM\u20135:15 PM<\/h3>\n

Tech Session 3: NLP: Machine Comprehension and Q&A | Sutton North
\nOral 3330: Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering<\/strong> Shangwen\u00a0Lv,\u00a0Daya\u00a0Guo,\u00a0Jingjing\u00a0Xu,\u00a0Duyu\u00a0Tang (opens in new tab)<\/span><\/a>,\u00a0Nan\u00a0Duan (opens in new tab)<\/span><\/a>,\u00a0Ming Gong (opens in new tab)<\/span><\/a>,\u00a0Linjun Shou (opens in new tab)<\/span><\/a>,\u00a0Daxin Jiang (opens in new tab)<\/span><\/a>,\u00a0Guihong\u00a0Cao (opens in new tab)<\/span><\/a>,\u00a0Songlin\u00a0Hu<\/p>\n

Tech Session 3: NLP: Machine Comprehension and Q&A | Sutton North
\nOral 8941: PIQA: Reasoning about Physical Commonsense in Natural Language<\/strong>
\nYonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao (opens in new tab)<\/span><\/a>, Yejin Choi<\/p>\n

Tech Session 3: NLP: Machine Comprehension and Q&A | Sutton North
\nPoster 3657: Segment-then-Rank: Non-factoid Question Answering on Instructional Videos<\/strong>
\nKyungjae Lee, Nan Duan (opens in new tab)<\/span><\/a>, Lei Ji (opens in new tab)<\/span><\/a>, Jason Li<\/strong>, Seungwon Hwang<\/p>\n

Tech Session 3: NLP: Machine Comprehension and Q&A | Sutton North
\nPoster: 6672: Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning<\/strong>
\nYibo Sun, Duyu Tang (opens in new tab)<\/span><\/a>, Nan Duan (opens in new tab)<\/span><\/a>, Yeyun Gong<\/strong>, Xiaocheng Feng, Bing Qin, Daxin Jiang (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 2: Application: Financial\/Econ, Medical Imaging and Health | Beekman
\nPoster 3537: Graph-Driven Generative Models for Heterogeneous Multi-Task Learning<\/strong>
\nWenlin Wang, Hongteng Xu, Zhe Gan (opens in new tab)<\/span><\/a>, Bai Li, Guoyin Wang, Liqun Chen, Qian Yang, Wenqi Wang, Lawrence Carin Duke<\/p>\n

7:30 PM\u20139:30 PM | Poster Session<\/h3>\n

APP3537:\u00a0Graph-Driven Generative Models for Heterogeneous Multi-Task Learning<\/strong>
\nWenlin\u00a0Wang,\u00a0Hongteng\u00a0Xu,\u00a0Zhe\u00a0Gan (opens in new tab)<\/span><\/a>,\u00a0Bai Li,\u00a0Guoyin\u00a0Wang,\u00a0Liqun\u00a0Chen,\u00a0Qian Yang,\u00a0Wenqi\u00a0Wang,\u00a0Lawrence Carin Duke<\/p>\n

ML1687:\u00a0Stochastic Online Learning with Probabilistic Graph Feedback<\/strong>
\nShuai Li,\u00a0Wei Chen (opens in new tab)<\/span><\/a>,\u00a0Zheng Wen,\u00a0Kwong-Sak\u00a0Leong<\/p>\n

ML3914:\u00a0Model Watermarking for Image Processing Networks<\/strong>
\nJie\u00a0Zhang,\u00a0Dongdong\u00a0Chen (opens in new tab)<\/span><\/a>,\u00a0Jing Liao,\u00a0Han Fang,\u00a0Weiming\u00a0Zhang,\u00a0Wenbo\u00a0Zhou,\u00a0Hao Cui,\u00a0Nenghai\u00a0Yu<\/p>\n

NLP3057:\u00a0PHASEN: A Phase-and-Harmonics-Aware Speech Enhancement Network<\/strong>
\nDacheng\u00a0Yin,\u00a0Chong Luo (opens in new tab)<\/span><\/a>,\u00a0Zhiwei\u00a0Xiong,\u00a0Wenjun Zeng (opens in new tab)<\/span><\/a><\/p>\n

NLP3657:\u00a0Segment-then-Rank: Non-factoid Question Answering on Instructional Videos<\/strong>
\nKyungjae\u00a0Lee,\u00a0Nan\u00a0Duan (opens in new tab)<\/span><\/a>,\u00a0Lei Ji (opens in new tab)<\/span><\/a>,\u00a0Jason Li<\/b>,\u00a0Seungwon\u00a0Hwang<\/p>\n

NLP3736: Fact-Aware Sentence Split and Rephrase with Permutation Invariant Training<\/strong>
\nYinuo\u00a0Guo,\u00a0Tao Ge (opens in new tab)<\/span><\/a>,\u00a0Furu\u00a0Wei (opens in new tab)<\/span><\/a><\/p>\n

NLP5015: Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources<\/strong>
\nQianhui\u00a0Wu,\u00a0Zijia\u00a0Lin (opens in new tab)<\/span><\/a>,\u00a0Guoxin\u00a0Wang (opens in new tab)<\/span><\/a>,\u00a0Hui Chen,\u00a0Borje\u00a0Karlsson (opens in new tab)<\/span><\/a>,\u00a0Biqing\u00a0Huang,\u00a0Chin-Yew Lin (opens in new tab)<\/span><\/a><\/p>\n

NLP5343:\u00a0RobuTrans:\u00a0A\u00a0Robust Transformer based Text-to-Speech Model<\/strong>
\nNaihan\u00a0Li,\u00a0Yanqing Liu (opens in new tab)<\/span><\/a>,\u00a0Yu Wu (opens in new tab)<\/span><\/a>,\u00a0Shujie\u00a0Liu (opens in new tab)<\/span><\/a>,\u00a0Sheng Zhao (opens in new tab)<\/span><\/a>,\u00a0Ming Liu<\/p>\n

NLP6672:\u00a0Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning<\/strong>
\nYibo\u00a0Sun,\u00a0Duyu\u00a0Tang (opens in new tab)<\/span><\/a>,\u00a0Nan\u00a0Duan (opens in new tab)<\/span><\/a>,\u00a0Yeyun\u00a0Gong<\/strong>,\u00a0Xiaocheng\u00a0Feng,\u00a0Bing Qin,\u00a0Daxin Jiang (opens in new tab)<\/span><\/a><\/p>\n

NLP8941:\u00a0PIQA: Reasoning about Physical Commonsense in Natural Language<\/strong>
\nYonatan Bisk,\u00a0Rowan Zellers,\u00a0Ronan Le Bras,\u00a0Jianfeng\u00a0Gao (opens in new tab)<\/span><\/a>,\u00a0Yejin\u00a0Choi<\/p>\n

NLP9360: Sequence Generation with Optimal-Transport-Enhanced Reinforcement Learning<\/strong>
\nLiqun\u00a0Chen,\u00a0Ke\u00a0Bai,\u00a0Chenyang\u00a0Tao,\u00a0Yizhe\u00a0Zhang (opens in new tab)<\/span><\/a>,\u00a0Guoyin\u00a0Wang,\u00a0Wenlin\u00a0Wang,\u00a0Ricardo\u00a0Henao,\u00a0Lawrence Carin Duke<\/p>\n

VIS362:\u00a0Unified Vision-Language\u00a0Pre-Training for Image Captioning and VQA<\/strong>
\nLuowei\u00a0Zhou,\u00a0Hamid\u00a0Palangi (opens in new tab)<\/span><\/a>,\u00a0Lei Zhang (opens in new tab)<\/span><\/a>,\u00a0Houdong\u00a0Hu (opens in new tab)<\/span><\/a>,\u00a0Jason Corso,\u00a0Jianfeng\u00a0Gao (opens in new tab)<\/span><\/a><\/p>\n

VIS525:\u00a0Leveraging Multi-view Image Sets for Unsupervised Intrinsic Image Decomposition and Highlight Separation<\/strong>
\nRenjiao\u00a0Yi,\u00a0Ping Tan,\u00a0Stephen Lin<\/b><\/p>\n

VIS561:\u00a0Uncertainty-aware Multi-shot Knowledge Distillation for Image-based Object Re-identification<\/strong>
\nXin\u00a0Jin,\u00a0Cuiling\u00a0Lan (opens in new tab)<\/span><\/a>,\u00a0Wenjun Zeng (opens in new tab)<\/span><\/a>,\u00a0Zhibo\u00a0Chen,<\/p>\n

VIS4022: DGCN: Dynamic Graph Convolutional Network for Efficient Multi-Person Pose Estimation<\/strong>
\nZhongwei\u00a0Qiu,\u00a0Kai\u00a0Qiu (opens in new tab)<\/span><\/a>,\u00a0Jianlong\u00a0Fu (opens in new tab)<\/span><\/a>,\u00a0Dongmei\u00a0Fu<\/p>\n

VIS5994:\u00a0Shallow Feature based Dense Attention Network for Crowd Counting<\/strong>
\nYUNQI MIAO,\u00a0Zijia\u00a0Lin (opens in new tab)<\/span><\/a>,\u00a0Guiguang\u00a0Ding,\u00a0Jungong\u00a0Han<\/p>\n

Monday, February 10<\/h2>\n

9:30 AM\u201310:45 AM<\/h3>\n

Tech Session 3: NLP: Generation | Sutton North
\nOral 6824: A Dataset for Low-Resource Stylized Sequence-to-Sequence Generation<\/strong>
\nYu Wu (opens in new tab)<\/span><\/a>,\u00a0Yunli\u00a0Wang,\u00a0Shujie\u00a0Liu (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 3: NLP: Generation | Sutton North
\nPoster 6828: Complementary Auxiliary Classifiers for Label-Conditional Text Generation<\/strong>
\nYuan Li, Chunyuan Li (opens in new tab)<\/span><\/a>, Yizhe Zhang (opens in new tab)<\/span><\/a>, Xiujun Li (opens in new tab)<\/span><\/a>, Guoqing Zheng (opens in new tab)<\/span><\/a>, Lawrence Carin Duke, Jianfeng Gao (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 3: NLP: Generation | Sutton North
\nPoster 7682: Cross-Lingual Natural Language Generation via Pre-Training<\/strong>
\nZewen Chi, Li Dong (opens in new tab)<\/span><\/a>, Furu Wei (opens in new tab)<\/span><\/a>, Wenhui Wang (opens in new tab)<\/span><\/a>, Xian-Ling Mao, Heyan Huang<\/p>\n

Tech Session 6: ML: Optimization, Neural Architecture Search, and Others | Concourse A
\nPoster 2073: Light Multi-segment Activation for Model Compression<\/strong>
\nZhenhui Xu, Guolin Ke (opens in new tab)<\/span><\/a>, Jia Zhang (opens in new tab)<\/span><\/a>, Jiang Bian (opens in new tab)<\/span><\/a>, Tieyan Liu<\/p>\n

Tech Session 8: Humans and AI | Clinton
\nOral 9127: Human-Machine Collaboration for Fast Land Cover Mapping<\/strong>
\nCaleb Robinson, Anthony Ortiz, Nikolay Malkin, Blake Elias, Andi Peng (opens in new tab)<\/span><\/a>, Dan Morris (opens in new tab)<\/span><\/a>, Bistra Dilkina, Nebojsa Jojic (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 9: Games: Description Languages and NLP | Madison
\nOral 9405: Interactive Fiction Games: A Colossal Adventure<\/strong>
\nMatthew Hausknecht (opens in new tab)<\/span><\/a>, Prithviraj Ammanabrolu, Marc-Alexandre C\u00f4t\u00e9 (opens in new tab)<\/span><\/a>, Xingdi Yuan (opens in new tab)<\/span><\/a><\/p>\n

11:15 AM\u201312:30 PM<\/h3>\n

Session 3: NLP: Machine Translation | Sutton North
\nPoster 4794: Bridging the Gap between Pre-Training and Fine-Tuning for End-to-End Speech Translation<\/strong>
\nChengyi Wang, Yu Wu (opens in new tab)<\/span><\/a>, Shujie Liu (opens in new tab)<\/span><\/a>, Zhenglu Yang, Ming Zhou (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 5: ML: Neural Nets Theory, Models and Algorithms | Murray Hill
\nOral 9804: Differential Equation Units: Learning Functional Forms of Activation Functions from Data<\/strong>
\nMohamadali Torkamani, Shiv Shankar, Pedram Rooshenas, Philip Wallis<\/strong><\/p>\n

2:00 PM\u20133:15 PM<\/h3>\n

Tech Session 4: ML: RL and Multiagent RL | Regent
\nPoster 2312: Learning Agent Communication under Limited Bandwidth by Message Pruning<\/strong>
\nHangyu Mao, Zhengchao Zhang, Zhen Xiao, Zhibo Gong, Yan Ni (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 3: NLP: Machine Translation\/Vison: Language + Vision | Sutton North
\nPoster 4133: Unicoder-VL: A Universal Encoder for Vision and Language by Cross-Modal Pre-Training<\/strong>
\nGen Li, Nan Duan (opens in new tab)<\/span><\/a>, Yuejian Fang, Ming Gong (opens in new tab)<\/span><\/a>, Daxin Jiang (opens in new tab)<\/span><\/a><\/p>\n

3:45 PM\u20135:15 PM<\/h3>\n

Tech Session 3: NLP: Information Extraction, Retrieval and Text Mining | Sutton North
\nOral 6492: Simultaneously Linking Entities and Extracting Relations from Biomedical Text without Mention-Level Supervision<\/strong>
\nTrapit Bansal, Pat Verga, Neha Choudhary<\/strong>, Andrew McCallum<\/p>\n

Tech Session 3: NLP: Information Extraction, Retrieval and Text Mining | Sutton North
\nPoster 7067: Table2Analysis: Modeling and Recommendation of Common Analysis Patterns for Multi-Dimensional Data<\/strong>
\nMengyu Zhou (opens in new tab)<\/span><\/a>, Tao Wang, Pengxin Ji, Shi Han (opens in new tab)<\/span><\/a>, Dongmei Zhang (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 9: Social Networks, Influence, and Homophily | Clinton
\nOral 5649: Adaptive Greedy Versus Non-adaptive Greedy for Influence Maximization<\/strong>
\nWei Chen (opens in new tab)<\/span><\/a>, Binghui Peng, Grant Schoenebeck, Biaoshuai Tao<\/p>\n

Tech Session 9: Social Networks, Influence, and Homophily | Clinton
\nPoster 4623: Gradient Method for Continuous Influence Maximization with Budget-Saving Considerations<\/strong>
\nWei Chen (opens in new tab)<\/span><\/a>, Weizhong Zhang, Haoyu Zhao<\/p>\n

Tech Session 10: Reasoning under Uncertainty | Madison
\nPoster 3579: Reliable and Efficient Anytime Skeleton Learning<\/strong>
\nRui Ding<\/strong>, Yanzhi Liu, Jingjing Tian, Zhouyu Fu, Shi Han (opens in new tab)<\/span><\/a>, Dongmei Zhang (opens in new tab)<\/span><\/a><\/p>\n

7:20 PM\u20139:20 PM\u00a0| Poster Session<\/h3>\n

AIW4623:\u00a0Gradient Method for Continuous Influence Maximization with Budget-Saving Considerations<\/strong>
\nWei Chen (opens in new tab)<\/span><\/a>,\u00a0Weizhong\u00a0Zhang,\u00a0Haoyu\u00a0Zhao<\/p>\n

AIW7067:\u00a0Table2Analysis: Modeling and Recommendation of Common Analysis Patterns for Multi-Dimensional Data<\/strong>
\nMengyu\u00a0Zhou (opens in new tab)<\/span><\/a>,\u00a0Tao Wang,\u00a0Pengxin\u00a0Ji,\u00a0Shi Han (opens in new tab)<\/span><\/a>,\u00a0Dongmei\u00a0Zhang (opens in new tab)<\/span><\/a><\/p>\n

APP5649:\u00a0Adaptive Greedy Versus Non-adaptive\u00a0Greedy for Influence Maximization<\/strong>
\nWei Chen (opens in new tab)<\/span><\/a>,\u00a0Binghui\u00a0Peng,\u00a0Grant\u00a0Schoenebeck,\u00a0Biaoshuai\u00a0Tao<\/p>\n

HAC9127:\u00a0Human-Machine Collaboration for Fast Land Cover Mapping<\/strong>
\nCaleb Robinson,\u00a0Anthony Ortiz,\u00a0Nikolay\u00a0Malkin,\u00a0Blake Elias,\u00a0Andi Peng (opens in new tab)<\/span><\/a>,\u00a0Dan Morris (opens in new tab)<\/span><\/a>,\u00a0Bistra\u00a0Dilkina,\u00a0Nebojsa\u00a0Jojic (opens in new tab)<\/span><\/a><\/p>\n

ML2073:\u00a0Light Multi-segment Activation for Model Compression<\/strong>
\nZhenhui\u00a0Xu,\u00a0Guolin\u00a0Ke (opens in new tab)<\/span><\/a>,\u00a0Jia Zhang (opens in new tab)<\/span><\/a>,\u00a0Jiang Bian (opens in new tab)<\/span><\/a>,\u00a0Tieyan\u00a0Liu<\/p>\n

ML2312:\u00a0Learning Agent Communication under Limited Bandwidth by Message Pruning<\/strong>
\nHangyu\u00a0Mao,\u00a0Zhengchao\u00a0Zhang,\u00a0Zhen Xiao,\u00a0Zhibo\u00a0Gong,\u00a0Yan Ni (opens in new tab)<\/span><\/a><\/p>\n

ML9405:\u00a0Interactive Fiction Games: A Colossal Adventure<\/strong>
\nMatthew\u00a0Hausknecht (opens in new tab)<\/span><\/a>,\u00a0Prithviraj\u00a0Ammanabrolu,\u00a0Marc-Alexandre\u00a0C\u00f4t\u00e9 (opens in new tab)<\/span><\/a>,\u00a0Xingdi\u00a0Yuan (opens in new tab)<\/span><\/a><\/p>\n

ML9804:\u00a0Differential Equation Units: Learning Functional Forms of Activation Functions from Data<\/strong>
\nMohamadali\u00a0Torkamani,\u00a0Shiv Shankar,\u00a0Pedram\u00a0Rooshenas,\u00a0Philip Wallis<\/b><\/p>\n

NLP4794:\u00a0Bridging the Gap between Pre-Training and Fine-Tuning for End-to-End Speech Translation<\/strong>
\nChengyi\u00a0Wang,\u00a0Yu Wu (opens in new tab)<\/span><\/a>,\u00a0Shujie\u00a0Liu (opens in new tab)<\/span><\/a>,\u00a0Zhenglu\u00a0Yang,\u00a0Ming Zhou (opens in new tab)<\/span><\/a><\/p>\n

NLP6828:\u00a0Complementary Auxiliary Classifiers for Label-Conditional Text Generation<\/strong>
\nYuan Li,\u00a0Chunyuan\u00a0Li (opens in new tab)<\/span><\/a>,\u00a0Yizhe\u00a0Zhang (opens in new tab)<\/span><\/a>,\u00a0Xiujun\u00a0Li (opens in new tab)<\/span><\/a>,\u00a0Guoqing\u00a0Zheng (opens in new tab)<\/span><\/a>,\u00a0Lawrence Carin Duke,\u00a0Jianfeng\u00a0Gao (opens in new tab)<\/span><\/a><\/p>\n

NLP7682:\u00a0Cross-Lingual Natural Language Generation via Pre-Training<\/strong>
\nZewen\u00a0Chi,\u00a0Li Dong (opens in new tab)<\/span><\/a>,\u00a0Furu\u00a0Wei (opens in new tab)<\/span><\/a>,\u00a0Wenhui\u00a0Wang (opens in new tab)<\/span><\/a>,\u00a0Xian-Ling Mao,\u00a0Heyan\u00a0Huang<\/p>\n

RU3579:\u00a0Reliable and Efficient Anytime Skeleton Learning<\/strong>
\nRui Ding<\/strong>,\u00a0Yanzhi\u00a0Liu,\u00a0Jingjing\u00a0Tian,\u00a0Zhouyu\u00a0Fu,\u00a0Shi Han (opens in new tab)<\/span><\/a>,\u00a0Dongmei\u00a0Zhang (opens in new tab)<\/span><\/a><\/p>\n

VIS4133:\u00a0Unicoder-VL: A Universal Encoder for Vision and Language by Cross-Modal Pre-Training<\/strong>
\nGen Li,\u00a0Nan\u00a0Duan (opens in new tab)<\/span><\/a>,\u00a0Yuejian\u00a0Fang,\u00a0Ming Gong (opens in new tab)<\/span><\/a>,\u00a0Daxin Jiang (opens in new tab)<\/span><\/a><\/p>\n

Tuesday, February 11<\/h2>\n

9:30 AM\u201310:45 AM<\/h3>\n

Tech Session 4: ML: RL and Multiagent RL | Regent
\nPoster 6906: Metareasoning in Modular Software Systems: On-the-Fly Configuration Using Reinforcement Learning with Rich Contextual Representations<\/strong>
\nAditya Modi, Debadeepta Dey (opens in new tab)<\/span><\/a>, Alekh Agarwal (opens in new tab)<\/span><\/a>, Adith Swaminathan (opens in new tab)<\/span><\/a>, Besmira Nushi (opens in new tab)<\/span><\/a>, Sean Andrist (opens in new tab)<\/span><\/a>, Eric Horvitz (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 4: ML: RL and Multiagent RL | Regent
\nPoster 9280: Actor Critic Deep Reinforcement Learning for Neural Malware Control<\/strong>
\nYu Wang<\/strong>, Jack Stokes<\/strong>, Mady Marinescu<\/strong><\/p>\n

Tech Session 5: Vision: Image Retrieval, Ranking, Recognition | Gramercy
\nPoster 7144: Functionality Discovery and Prediction of Physical Objects<\/strong>
\nLei Ji (opens in new tab)<\/span><\/a>, Botian Shi, Xianglin Guo, Xilin Chen<\/p>\n

11:15 AM\u201312:30 PM<\/h3>\n

Tech Session 3: NLP: Representation Learning | Sutton South
\nOral 3656: P-SIF: Document Embeddings Using Partition Averaging<\/strong>
\nVivek Gupta, Ankit Saw, Pegah Nokhiz, Praneeth Netrapalli (opens in new tab)<\/span><\/a>, Piyush Rai, Partha Talukdar<\/p>\n

Tech Session 3: NLP: Representation Learning | Sutton South
\nOral 5766: TextNAS: A Neural Architecture Search Space Tailored for Text Representation<\/strong>
\nYujing Wang, Yaming Yang, Yiren Chen, Jing Bai (opens in new tab)<\/span><\/a>, Ce Zhang, Guinan Su, Xiaoyu Kou, Yunhai Tong, Mao Yang (opens in new tab)<\/span><\/a>, Lidong Zhou (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 3: NLP: Representation Learning | Sutton South
\nPoster 7202: Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding<\/strong>
\nOren Barkan, Noam Razin (opens in new tab)<\/span><\/a>, Itzik Malkiel (opens in new tab)<\/span><\/a>, Ori Katz<\/strong>, Avi Caciularu (opens in new tab)<\/span><\/a>, Noam Koenigstein (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 4: ML: Neural Nets Theory, Models and Algorithms | Regent
\nPoster 1536: Posterior-Guided Neural Architecture Search<\/strong>
\nYizhou Zhou, Xiaoyan Sun (opens in new tab)<\/span><\/a>, Chong Luo (opens in new tab)<\/span><\/a>, Zheng-Jun Zha, Wenjun Zeng (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 12: Mechanism Design and Fair Allocation | Morgan
\nOral 4617: Online Second Price Auction with Semi-bandit Feedback Under the Non-Stationary Setting<\/strong>
\nHaoyu Zhao, Wei Chen<\/p>\n

2:00 PM\u20133:30 PM<\/h3>\n

Tech Session 2: NLP: Representation Learning, Summarization | Sutton North
\nPoster 9252: Latent Relation Language Models<\/strong>
\nHiroaki Hayashi, Zecong Hu, Chenyan Xiong (opens in new tab)<\/span><\/a>, Graham Neubig<\/p>\n

Tech Session 3: NLP: Semantics and Summarization | Sutton South
\nOral 3184: Graph-based Transformer with Cross-Candidate Verification for Semantic Parsing<\/strong>
\nBo Shao, Yeyun Gong<\/strong>, Weizhen Qi, Guihong Cao (opens in new tab)<\/span><\/a>, Jianshu Ji (opens in new tab)<\/span><\/a>, Xiaola Lin<\/p>\n

Tech Session 5: Vision: Human: Pose and Motion Analysis, Detection, Modeling | Gramercy
\nPoster 2669: Semantics-Aligned Representation Learning for Person Re-identification<\/strong>
\nXin Jin, Cuiling Lan (opens in new tab)<\/span><\/a>, Wenjun Zeng (opens in new tab)<\/span><\/a>, Guoqiang Wei, Zhibo Chen<\/p>\n

Tech Session 5: Vision: Human: Pose and Motion Analysis, Detection, Modeling | Gramercy
\nPoster 8881: Crowd Counting with Decomposed Uncertainty<\/strong>
\nMin-hwan Oh, Peder Olsen (opens in new tab)<\/span><\/a>, Karthikeyan Natesan Ramamurthy<\/p>\n

Tech Session 7: Vision: Vision + Language | Nassau
\nPoster 1374: Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language<\/strong>
\nSongyang Zhang, Houwen Peng (opens in new tab)<\/span><\/a>, Jianlong Fu (opens in new tab)<\/span><\/a>, Jiebo Luo<\/p>\n

Tech Session 7: Vision: Vision + Language | Nassau
\nPoster 2236: DualVD: An Adaptive Dual Encoding Model for Deep Visual Understanding in Visual Dialogue<\/strong>
\nXiaoze Jiang, Jing Yu, Yingying Zhuang, Zengchang Qin, Yue Hu, Qi Wu, Xingxing Zhang (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 7: Vision: Vision + Language | Nassau
\nPoster 3499: What Makes A Good Story? Designing Composite Rewards for Visual Storytelling<\/strong>
\nJunjie Hu, Yu Cheng (opens in new tab)<\/span><\/a>, Zhe Gan (opens in new tab)<\/span><\/a>, Jingjing Liu (opens in new tab)<\/span><\/a>, Jianfeng Gao (opens in new tab)<\/span><\/a>, Graham Neubig,<\/p>\n

Tech Session 13: Mechanism Design | Bryant
\nOral 1796: Preventing Arbitrage from Collusion When Eliciting Probabilities<\/strong>
\nRupert Freeman (opens in new tab)<\/span><\/a>, David Pennock, Dominik Peters, Bo Waggoner<\/p>\n

Wednesday, February 12<\/h2>\n

11:30 AM\u201312:30 PM<\/h3>\n

Tech Session 1: NLP: Language Models | Trianon
\nOral 4504: Alternating Language Modeling for Cross-Lingual Pre-Training<\/strong>
\nJian Yang,\u00a0Shuming\u00a0Ma (opens in new tab)<\/span><\/a>,\u00a0Dongdong\u00a0Zhang (opens in new tab)<\/span><\/a>,\u00a0ShuangZhi\u00a0Wu,\u00a0Zhoujun\u00a0Li,\u00a0Ming Zhou (opens in new tab)<\/span><\/a><\/p>\n

Tech Session 4: NLP: Semantics and Summarization | Sutton South
\nOral 7131: Multi-level Head-wise Match and Aggregation in Transformer for Textual Sequence Matching<\/strong>
\nShuohang\u00a0Wang (opens in new tab)<\/span><\/a>,\u00a0Yunshi\u00a0Lan,\u00a0Yi Tay,\u00a0Jing Jiang,\u00a0Jingjing\u00a0Liu (opens in new tab)<\/span><\/a><\/p>\n

2:00 PM\u20133:20 PM<\/h3>\n

Tech Session 1: NLP: Machine Translation, Entity Recognition and Linking | Trianon
\nOral 2729: Fine-Tuning by Curriculum Learning for Non-Autoregressive Neural Machine Translation<\/strong>
\nJunliang Guo, Xu Tan (opens in new tab)<\/span><\/a>, Linli Xu, Tao Qin (opens in new tab)<\/span><\/a>, Tieyan Liu, Enhong Chen<\/p>\n

Tech Session 1: NLP: Machine Translation, Entity Recognition and Linking | Trianon
\nOral 3465: Transductive Ensemble Learning for Neural Machine Translation<\/strong>
\nYiren Wang, Lijun Wu, Yingce Xia (opens in new tab)<\/span><\/a>, Tao Qin (opens in new tab)<\/span><\/a>, Cheng Xiang Zhai, Tieyan Liu<\/p>\n

6:30 PM\u20138:30 PM | Poster Session<\/h3>\n

AIW7144:\u00a0Functionality Discovery and Prediction of Physical Objects<\/strong>
\nLei Ji (opens in new tab)<\/span><\/a>,\u00a0Botian\u00a0Shi,\u00a0Xianglin\u00a0Guo,\u00a0Xilin\u00a0Chen<\/p>\n

APP9280:\u00a0Actor Critic Deep Reinforcement Learning for Neural Malware Control<\/strong>
\nYu Wang<\/b>,<\/b>\u00a0Jack Stokes<\/b>,<\/b>\u00a0Mady<\/b>\u00a0Marinescu<\/b><\/p>\n

GTEP1796:\u00a0Preventing Arbitrage\u00a0from Collusion When Eliciting Probabilities<\/strong>
\nRupert Freeman (opens in new tab)<\/span><\/a>,\u00a0David Pennock,\u00a0Dominik Peters,\u00a0Bo Waggoner<\/p>\n

ML1536:\u00a0Posterior-Guided Neural Architecture Search<\/strong>
\nYizhou\u00a0Zhou,\u00a0Xiaoyan\u00a0Sun (opens in new tab)<\/span><\/a>,\u00a0Chong Luo (opens in new tab)<\/span><\/a>,\u00a0Zheng-Jun\u00a0Zha,\u00a0Wenjun Zeng (opens in new tab)<\/span><\/a><\/p>\n

ML2669:\u00a0Semantics-Aligned Representation Learning for Person Re-identification<\/strong>
\nXin\u00a0Jin,\u00a0Cuiling\u00a0Lan (opens in new tab)<\/span><\/a>,\u00a0Wenjun Zeng (opens in new tab)<\/span><\/a>,\u00a0Guoqiang\u00a0Wei,\u00a0Zhibo\u00a0Chen<\/p>\n

ML3465:\u00a0Transductive\u00a0Ensemble Learning for Neural Machine Translation<\/strong>
\nYiren\u00a0Wang,\u00a0Lijun Wu,\u00a0Yingce\u00a0Xia (opens in new tab)<\/span><\/a>,\u00a0Tao Qin (opens in new tab)<\/span><\/a>,\u00a0Cheng Xiang\u00a0Zhai,\u00a0Tieyan\u00a0Liu<\/p>\n

ML4617:\u00a0Online Second Price Auction with Semi-bandit Feedback Under the Non-Stationary Setting<\/strong>
\nHaoyu\u00a0Zhao,\u00a0Wei Chen (opens in new tab)<\/span><\/a><\/p>\n

ML6906:\u00a0Metareasoning\u00a0in Modular Software Systems: On-the-Fly Configuration Using Reinforcement Learning with Rich Contextual Representations<\/strong>
\nAditya Modi,\u00a0Debadeepta\u00a0Dey (opens in new tab)<\/span><\/a>,\u00a0Alekh\u00a0Agarwal (opens in new tab)<\/span><\/a>,\u00a0Adith\u00a0Swaminathan (opens in new tab)<\/span><\/a>,\u00a0Besmira Nushi (opens in new tab)<\/span><\/a>,\u00a0Sean Andrist (opens in new tab)<\/span><\/a>,\u00a0Eric Horvitz (opens in new tab)<\/span><\/a><\/p>\n

NLP2729:\u00a0Fine-Tuning by Curriculum Learning for Non-Autoregressive Neural Machine Translation<\/strong>
\nJunliang\u00a0Guo,\u00a0Xu Tan (opens in new tab)<\/span><\/a>,\u00a0Linli\u00a0Xu,\u00a0Tao Qin (opens in new tab)<\/span><\/a>,\u00a0Tieyan\u00a0Liu,\u00a0Enhong\u00a0Chen<\/p>\n

NLP3184:\u00a0Graph-based Transformer with Cross-Candidate Verification for Semantic Parsing<\/strong>
\nBo Shao,\u00a0Yeyun<\/b>\u00a0Gong<\/b>,\u00a0Weizhen\u00a0Qi,\u00a0Guihong\u00a0Cao (opens in new tab)<\/span><\/a>,\u00a0Jianshu\u00a0Ji (opens in new tab)<\/span><\/a>,\u00a0Xiaola\u00a0Lin<\/p>\n

NLP3656:\u00a0P-SIF: Document Embeddings Using Partition Averaging<\/strong>
\nVivek Gupta,\u00a0Ankit Saw,\u00a0Pegah\u00a0Nokhiz,\u00a0Praneeth\u00a0Netrapalli (opens in new tab)<\/span><\/a>,\u00a0Piyush Rai,\u00a0Partha\u00a0Talukdar<\/p>\n

NLP5766:\u00a0TextNAS: A Neural Architecture Search Space\u00a0Tailored for Text Representation<\/strong>
\nYujing\u00a0Wang,\u00a0Yaming\u00a0Yang,\u00a0Yiren\u00a0Chen,\u00a0Jing Bai (opens in new tab)<\/span><\/a>,\u00a0Ce Zhang,\u00a0Guinan\u00a0Su,\u00a0Xiaoyu\u00a0Kou,\u00a0Yunhai\u00a0Tong,\u00a0Mao Yang (opens in new tab)<\/span><\/a>,\u00a0Lidong\u00a0Zhou (opens in new tab)<\/span><\/a><\/p>\n

NLP7202:\u00a0Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding<\/strong>
\nOren\u00a0Barkan,\u00a0Noam\u00a0Razin (opens in new tab)<\/span><\/a>,\u00a0Itzik\u00a0Malkiel (opens in new tab)<\/span><\/a>,\u00a0Ori Katz<\/strong>,\u00a0Avi\u00a0Caciularu (opens in new tab)<\/span><\/a>,\u00a0Noam\u00a0Koenigstein (opens in new tab)<\/span><\/a><\/p>\n

NLP9252:\u00a0Latent Relation Language Models<\/strong>
\nHiroaki Hayashi,\u00a0Zecong\u00a0Hu,\u00a0Chenyan\u00a0Xiong (opens in new tab)<\/span><\/a>,\u00a0Graham\u00a0Neubig<\/p>\n

VIS1374:\u00a0Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language<\/strong>
\nSongyang\u00a0Zhang,\u00a0Houwen\u00a0Peng (opens in new tab)<\/span><\/a>,\u00a0Jianlong\u00a0Fu (opens in new tab)<\/span><\/a>,\u00a0Jiebo\u00a0Luo<\/p>\n

VIS2236:\u00a0DualVD: An Adaptive Dual Encoding Model for Deep Visual Understanding in Visual Dialogue<\/strong>
\nXiaoze\u00a0Jiang,\u00a0Jing Yu,\u00a0Yingying Zhuang,\u00a0Zengchang\u00a0Qin,\u00a0Yue Hu,\u00a0Qi Wu,\u00a0Xingxing\u00a0Zhang (opens in new tab)<\/span><\/a><\/p>\n

VIS3499:\u00a0What Makes A Good Story? Designing Composite Rewards for Visual Storytelling<\/strong>
\nJunjie\u00a0Hu,\u00a0Yu Cheng (opens in new tab)<\/span><\/a>,\u00a0Zhe\u00a0Gan (opens in new tab)<\/span><\/a>,\u00a0Jingjing\u00a0Liu (opens in new tab)<\/span><\/a>,\u00a0Jianfeng\u00a0Gao (opens in new tab)<\/span><\/a>,\u00a0Graham\u00a0Neubig,<\/p>\n

VIS8881:\u00a0Crowd Counting with Decomposed Uncertainty<\/strong>
\nMin-hwan\u00a0Oh,\u00a0Peder\u00a0Olsen (opens in new tab)<\/span><\/a>,\u00a0Karthikeyan Natesan RamamurthyOpens in a new tab<\/span><\/p>\n

Friday, February 7<\/h2>\n

W1: Affective Content Analysis (AffCon\u00a02020): Interactive Affective Response (opens in new tab)<\/span><\/a>
\nInvited Speaker:\u00a0Daniel McDuff (opens in new tab)<\/span><\/a><\/p>\n

W5:\u00a0Artificial Intelligence of Things (AIoT) (opens in new tab)<\/span><\/a>
\nProgram Co-Chairs:\u00a0Jian Zhang<\/b>
\nSteering Committee Member:\u00a0Victor\u00a0Bahl (opens in new tab)<\/span><\/a>
\nKeynote Speaker:\u00a0Ranveer Chandra (opens in new tab)<\/span><\/a>
\nAccepted Paper: In-Car Cognition with Edge Artificial Intelligence Accelerators
\nMing-<\/b>Chih<\/b>\u00a0Lin,\u00a0<\/b>Sio<\/b>-Sun Wong, Yu-<\/b>Kwen<\/b>\u00a0Hsu,\u00a0<\/b>Ti<\/b>-Hua Yang, Pi-Sui Hsu, He-Wei Lee, Wei-Chen Tsai, Hsiu-Tzu Wu, Sung-Lin Yeh<\/b><\/p>\n

W7:\u00a0Cloud Intelligence: AI\/ML for Efficient and Manageable Cloud Services (opens in new tab)<\/span><\/a>
\nProgram Chair:\u00a0Jian Zhang<\/b>
\nSteering Committee Members:Ricardo\u00a0Bianchini (opens in new tab)<\/span><\/a>, Marcus\u00a0Fontoura (opens in new tab)<\/span><\/a>,\u00a0Dongmei\u00a0Zhang (opens in new tab)<\/span><\/a>
\nKeynote Speaker:\u00a0Marcus\u00a0Fontoura (opens in new tab)<\/span><\/a>
\nInvited Speaker:\u00a0Dongmei\u00a0Zhang (opens in new tab)<\/span><\/a><\/p>\n

W14: Workshop on Intelligent Process Automation (opens in new tab)<\/span><\/a>
\nKeynote Speaker: Sumit Gulwani (opens in new tab)<\/span><\/a><\/p>\n

W22: Reproducibility in AI (RAI 2020) \u2014 Future Direction and Reproducibility Challenge (opens in new tab)<\/span><\/a>
\nInvited Speaker: Vani Mandava (opens in new tab)<\/span><\/a><\/p>\n

Saturday, February 8<\/h2>\n

W9:\u00a0The Eighth Dialog System Technology Challenge (DSTC8) (opens in new tab)<\/span><\/a>
\nWorkshop Chair:\u00a0Michel Galley (opens in new tab)<\/span><\/a>
\nTrack 1 Co-organizers: Jinchao Li (opens in new tab)<\/span><\/a>, Baolin Peng (opens in new tab)<\/span><\/a>, Jianfeng Gao (opens in new tab)<\/span><\/a>, Hannes Schulz (opens in new tab)<\/span><\/a>, Adam Atkinson (opens in new tab)<\/span><\/a>, Mahmoud Adada (opens in new tab)<\/span><\/a><\/p>\n

W11: Evaluating Evaluation of AI Systems (opens in new tab)<\/span><\/a>
\nProgram Committee Members:\u00a0Sid Suri (opens in new tab)<\/span><\/a>, Omar Alonso (opens in new tab)<\/span><\/a>Opens in a new tab<\/span><\/p>\n

Azure Global<\/h3>\n

Research Intern (opens in new tab)<\/span><\/a>
\nSenior Software Engineer (opens in new tab)<\/span><\/a>
\nPrincipal SDE (opens in new tab)<\/span><\/a><\/p>\n

Azure Production Infrastructure Engineering Team<\/h3>\n

Senior Software Engineer (opens in new tab)<\/span><\/a>
\nData and Applied Scientist II (opens in new tab)<\/span><\/a><\/p>\n

Bing<\/h3>\n

Senior Applied Scientist (opens in new tab)<\/span><\/a>
\nSenior Applied Scientist (opens in new tab)<\/span><\/a><\/p>\n

Security and Compliance Loonshot Research Team<\/h3>\n

Data Engineer (opens in new tab)<\/span><\/a>
\nSenior Security Engineer (opens in new tab)<\/span><\/a>
\nSenior Research Program Manager (opens in new tab)<\/span><\/a>
\nPrincipal Applied Researcher (opens in new tab)<\/span><\/a>
\nPrincipal Applied Researcher (opens in new tab)<\/span><\/a>
\nApplied Researcher II (opens in new tab)<\/span><\/a>
\nApplied Researcher II (opens in new tab)<\/span><\/a><\/p>\n

Full-Time Opportunities for PhD Students or Recent Graduates<\/h3>\n

AI Researcher (opens in new tab)<\/span><\/a>
\nComputer Vision Engineer (opens in new tab)<\/span><\/a>
\nNatural Language Processing Software Development Engineer (opens in new tab)<\/span><\/a>
\nMachine Learning Engineer (opens in new tab)<\/span><\/a>
\nProgram Manager & Software Engineer (opens in new tab)<\/span><\/a>Opens in a new tab<\/span><\/p>\n","tab-content":[{"id":0,"name":"About","content":"Microsoft is proud to be a sponsor of the Thirty-Fourth AAAI Conference on Artificial Intelligence<\/a>. Over 50 of our researchers will be at AAAI - stop by our booth to chat with our experts, see demos of our latest research and find out about career opportunities with Microsoft.\r\n

Microsoft attendees<\/h3>\r\nSaleema Amershi<\/a>\r\nAdam Atkinson<\/a>\r\nDionisia Barkalakis\r\nChristopher Bennage\r\nMichael Betser\r\nBiswarup Bhattacharya\r\nWenfeng Cheng\r\nMurali Chintalapati\r\nNeha Choudhary\r\nYingnong Dang\r\nRui Ding\r\nTony Duan\r\nMarcus Fontoura\r\nAdam Fourney<\/a>\r\nRupert Freeman<\/a>\r\nMichel Galley<\/a>\r\nFeng Gao\r\nJianfeng Gao<\/a>\r\nYeyun Gong\r\nSumit Gulwani<\/a>\r\nDevin Gunson\r\nShi Han<\/a>\r\nMicheleen Harris\r\nMatthew Hausknecht<\/a>\r\nEric Horvitz<\/a>\r\nKori Inkpen<\/a>\r\nLei Ji<\/a>\r\nEce Kamar<\/a>\r\nB\u00f6rje Karlsson<\/a>\r\nRyan Kelly\r\nSean Kuno<\/a>\r\nCuiling Lan<\/a>\r\nMiran Lee\r\nJinchao Li<\/a>\r\nZe Li\r\nNut Limsopatham\r\nChin-Yew Lin<\/a>\r\nJian Lin\r\nMing-Chih Lin\r\nZijia Lin<\/a>\r\nShujie Liu<\/a>\r\nXiaodong Liu<\/a>\r\nShuming Ma<\/a>\r\nVani Mandava<\/a>\r\nDaniel McDuff<\/a>\r\nMeredith Morris<\/a>\r\nBesmira Nushi<\/a>\r\nPeder Olsen\r\nDan O'Neill\r\nHamid Palangi<\/a>\r\nAndi Peng<\/a>\r\nBaolin Peng<\/a>\r\nForough Poursabzi-Sangdeh<\/a>\r\nFarrukh Rahman\r\nNoam Razin<\/a>\r\nHannes Schulz<\/a>\r\nShahin Shayandeh<\/a>\r\nAmy Siebenthaler\r\nJack Stokes\r\nAli Vira\r\nHanna Wallach<\/a>\r\nGuoxin Wang<\/a>\r\nShuohang Wang\r\nWei Wang<\/a>\r\nJenn Wortman Vaughan<\/a>\r\nYingce Xia<\/a>\r\nChenyan Xiong<\/a>\r\nZhangwei Xu\r\nQuanzeng You\r\nWenjun Zeng<\/a>\r\nDongmei Zhang<\/a>\r\nJian Zhang\r\nGuoqing Zheng<\/a>\r\nMengyu Zhou<\/a>"},{"id":1,"name":"Sessions","content":"

Saturday, February 8<\/h2>\r\n

8:30 AM\u201312:30 PM<\/h3>\r\nSA3: Recent Advances in Fair Resource Allocation<\/strong>\r\nRupert Freeman<\/a>,\u00a0Nisarg\u00a0Shah\r\n

8:30 AM\u201310:15 AM<\/h3>\r\nSA5Q: Guidelines for Human-AI Interaction<\/strong>\r\nBesmira Nushi<\/a>,\u00a0Dan Weld,\u00a0Saleema Amershi<\/a>, Adam Fourney<\/a>\r\n

Sunday, February 9<\/h2>\r\n

9:30 AM\u201310:45 AM<\/h3>\r\nTech Session 1: NLP: Entity Recognition and Linking | Trianon\r\nPoster 5015: Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources<\/strong>\r\nQianhui\u00a0Wu,\u00a0Zijia\u00a0Lin<\/a>,\u00a0Guoxin\u00a0Wang<\/a>,\u00a0Hui Chen,\u00a0Borje\u00a0Karlsson<\/a>,\u00a0Biqing\u00a0Huang,\u00a0Chin-Yew Lin<\/a>\r\n\r\nTech Session 3: NLP: Machine Translation | Sutton North\r\nPoster 3736: Fact-Aware Sentence Split and Rephrase with Permutation Invariant Training<\/strong>\r\nYinuo\u00a0Guo,\u00a0Tao Ge<\/a>,\u00a0Furu\u00a0Wei<\/a>\r\n\r\nTech Session 3: NLP: Machine Translation | Sutton North\r\nPoster 9360: Sequence Generation with Optimal-Transport-Enhanced Reinforcement Learning<\/strong>\r\nLiqun\u00a0Chen,\u00a0Ke\u00a0Bai,\u00a0Chenyang\u00a0Tao,\u00a0Yizhe\u00a0Zhang<\/a>,\u00a0Guoyin\u00a0Wang,\u00a0Wenlin\u00a0Wang,\u00a0Ricardo\u00a0Henao,\u00a0Lawrence Carin Duke\r\n

9:30 AM\u201310:45 AM<\/h3>\r\nTech Session 4: Vision: 3D | Gramercy\r\nPoster 4022: DGCN: Dynamic Graph Convolutional Network for Efficient Multi-Person Pose Estimation<\/strong>\r\nZhongwei\u00a0Qiu,\u00a0Kai\u00a0Qiu<\/a>,\u00a0Jianlong\u00a0Fu<\/a>,\u00a0Dongmei\u00a0Fu\r\n\r\nTech Session 5: ML: Online Learning | Murray Hill\r\nPoster 1687: Stochastic Online Learning with Probabilistic Graph Feedback<\/strong>\r\nShuai Li, Wei Chen<\/a>, Zheng Wen, Kwong-Sak Leong\r\n

11:15 AM\u201312:30 PM<\/h3>\r\nTech Session 4: Vision: Synthesis and Generation | Gramercy\r\nPoster 525: Leveraging Multi-view Image Sets for Unsupervised Intrinsic Image Decomposition and Highlight Separation<\/strong>\r\nRenjiao Yi, Ping Tan, Stephen Lin<\/strong>\r\n\r\nTech Session 5: ML: Neural Nets Theory, Models and Algorithms | Murray Hill\r\nPoster 3914: Model Watermarking for Image Processing Networks<\/strong>\r\nJie Zhang, Dongdong Chen<\/a>, Jing Liao, Han Fang, Weiming Zhang, Wenbo Zhou, Hao Cui, Nenghai Yu\r\n

2:00 PM\u20133:15 PM<\/h3>\r\nTech Session 1: NLP: Relational Learning | Trianon\r\nOral 1322: Improving Entity Linking by Modeling Latent Entity Type Information<\/strong>\r\nShuang Chen,\u00a0Jinpeng\u00a0Wang<\/a>,\u00a0Feng Jiang,\u00a0Chin-Yew Lin<\/a>\r\n\r\nTech Session 3: NLP: Speech, Syntax and Semantics | Sutton North\r\nPoster 3057: PHASEN: A Phase-and-Harmonics-Aware Speech Enhancement Network<\/strong>\r\nDacheng Yin, Chong Luo<\/a>, Zhiwei Xiong, Wenjun Zeng<\/a>\r\n\r\nTech Session 3: NLP: Speech, Syntax and Semantics | Sutton North\r\nPoster 5343: RobuTrans: A Robust Transformer based Text-to-Speech Model<\/strong>\r\nNaihan Li, Yanqing Liu<\/a>, Yu Wu<\/a>, Shujie Liu<\/a>, Sheng Zhao<\/a>, Ming Liu\r\n\r\nTech Session 4: Vision: Object Detection | Gramercy\r\nPoster 561: Uncertainty-aware Multi-shot Knowledge Distillation for Image-based Object Re-identification<\/strong>\r\nXin Jin, Cuiling Lan<\/a>, Wenjun Zeng<\/a>, Zhibo Chen\r\n\r\nTech Session 4: Vision: Object Detection | Gramercy\r\nPoster 5994: Shallow Feature based Dense Attention Network for Crowd Counting<\/strong>\r\nYunqi Miao, Zijia Lin<\/a>, Guiguang Ding, Jungong Han\r\n\r\nTech Session 6: Vision: Vision + Language | Nassau\r\nPoster 362: Unified Vision-Language Pre-Training for Image Captioning and VQA<\/strong>\r\nLuowei Zhou, Hamid Palangi<\/a>, Lei Zhang<\/a>, Houdong Hu<\/a>, Jason Corso, Jianfeng Gao<\/a>\r\n

3:45 PM\u20135:15 PM<\/h3>\r\nTech Session 3: NLP: Machine Comprehension and Q&A | Sutton North\r\nOral 3330: Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering<\/strong> Shangwen\u00a0Lv,\u00a0Daya\u00a0Guo,\u00a0Jingjing\u00a0Xu,\u00a0Duyu\u00a0Tang<\/a>,\u00a0Nan\u00a0Duan<\/a>,\u00a0Ming Gong<\/a>,\u00a0Linjun Shou<\/a>,\u00a0Daxin Jiang<\/a>,\u00a0Guihong\u00a0Cao<\/a>,\u00a0Songlin\u00a0Hu\r\n\r\nTech Session 3: NLP: Machine Comprehension and Q&A | Sutton North\r\nOral 8941: PIQA: Reasoning about Physical Commonsense in Natural Language<\/strong>\r\nYonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao<\/a>, Yejin Choi\r\n\r\nTech Session 3: NLP: Machine Comprehension and Q&A | Sutton North\r\nPoster 3657: Segment-then-Rank: Non-factoid Question Answering on Instructional Videos<\/strong>\r\nKyungjae Lee, Nan Duan<\/a>, Lei Ji<\/a>, Jason Li<\/strong>, Seungwon Hwang\r\n\r\nTech Session 3: NLP: Machine Comprehension and Q&A | Sutton North\r\nPoster: 6672: Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning<\/strong>\r\nYibo Sun, Duyu Tang<\/a>, Nan Duan<\/a>, Yeyun Gong<\/strong>, Xiaocheng Feng, Bing Qin, Daxin Jiang<\/a>\r\n\r\nTech Session 2: Application: Financial\/Econ, Medical Imaging and Health | Beekman\r\nPoster 3537: Graph-Driven Generative Models for Heterogeneous Multi-Task Learning<\/strong>\r\nWenlin Wang, Hongteng Xu, Zhe Gan<\/a>, Bai Li, Guoyin Wang, Liqun Chen, Qian Yang, Wenqi Wang, Lawrence Carin Duke\r\n

7:30 PM\u20139:30 PM | Poster Session<\/h3>\r\nAPP3537:\u00a0Graph-Driven Generative Models for Heterogeneous Multi-Task Learning<\/strong>\r\nWenlin\u00a0Wang,\u00a0Hongteng\u00a0Xu,\u00a0Zhe\u00a0Gan<\/a>,\u00a0Bai Li,\u00a0Guoyin\u00a0Wang,\u00a0Liqun\u00a0Chen,\u00a0Qian Yang,\u00a0Wenqi\u00a0Wang,\u00a0Lawrence Carin Duke\r\n\r\nML1687:\u00a0Stochastic Online Learning with Probabilistic Graph Feedback<\/strong>\r\nShuai Li,\u00a0Wei Chen<\/a>,\u00a0Zheng Wen,\u00a0Kwong-Sak\u00a0Leong\r\n\r\nML3914:\u00a0Model Watermarking for Image Processing Networks<\/strong>\r\nJie\u00a0Zhang,\u00a0Dongdong\u00a0Chen<\/a>,\u00a0Jing Liao,\u00a0Han Fang,\u00a0Weiming\u00a0Zhang,\u00a0Wenbo\u00a0Zhou,\u00a0Hao Cui,\u00a0Nenghai\u00a0Yu\r\n\r\nNLP3057:\u00a0PHASEN: A Phase-and-Harmonics-Aware Speech Enhancement Network<\/strong>\r\nDacheng\u00a0Yin,\u00a0Chong Luo<\/a>,\u00a0Zhiwei\u00a0Xiong,\u00a0Wenjun Zeng<\/a>\r\n\r\nNLP3657:\u00a0Segment-then-Rank: Non-factoid Question Answering on Instructional Videos<\/strong>\r\nKyungjae\u00a0Lee,\u00a0Nan\u00a0Duan<\/a>,\u00a0Lei Ji<\/a>,\u00a0Jason Li<\/b>,\u00a0Seungwon\u00a0Hwang\r\n\r\nNLP3736: Fact-Aware Sentence Split and Rephrase with Permutation Invariant Training<\/strong>\r\nYinuo\u00a0Guo,\u00a0Tao Ge<\/a>,\u00a0Furu\u00a0Wei<\/a>\r\n\r\nNLP5015: Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources<\/strong>\r\nQianhui\u00a0Wu,\u00a0Zijia\u00a0Lin<\/a>,\u00a0Guoxin\u00a0Wang<\/a>,\u00a0Hui Chen,\u00a0Borje\u00a0Karlsson<\/a>,\u00a0Biqing\u00a0Huang,\u00a0Chin-Yew Lin<\/a>\r\n\r\nNLP5343:\u00a0RobuTrans:\u00a0A\u00a0Robust Transformer based Text-to-Speech Model<\/strong>\r\nNaihan\u00a0Li,\u00a0Yanqing Liu<\/a>,\u00a0Yu Wu<\/a>,\u00a0Shujie\u00a0Liu<\/a>,\u00a0Sheng Zhao<\/a>,\u00a0Ming Liu\r\n\r\nNLP6672:\u00a0Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning<\/strong>\r\nYibo\u00a0Sun,\u00a0Duyu\u00a0Tang<\/a>,\u00a0Nan\u00a0Duan<\/a>,\u00a0Yeyun\u00a0Gong<\/strong>,\u00a0Xiaocheng\u00a0Feng,\u00a0Bing Qin,\u00a0Daxin Jiang<\/a>\r\n\r\nNLP8941:\u00a0PIQA: Reasoning about Physical Commonsense in Natural Language<\/strong>\r\nYonatan Bisk,\u00a0Rowan Zellers,\u00a0Ronan Le Bras,\u00a0Jianfeng\u00a0Gao<\/a>,\u00a0Yejin\u00a0Choi\r\n\r\nNLP9360: Sequence Generation with Optimal-Transport-Enhanced Reinforcement Learning<\/strong>\r\nLiqun\u00a0Chen,\u00a0Ke\u00a0Bai,\u00a0Chenyang\u00a0Tao,\u00a0Yizhe\u00a0Zhang<\/a>,\u00a0Guoyin\u00a0Wang,\u00a0Wenlin\u00a0Wang,\u00a0Ricardo\u00a0Henao,\u00a0Lawrence Carin Duke\r\n\r\nVIS362:\u00a0Unified Vision-Language\u00a0Pre-Training for Image Captioning and VQA<\/strong>\r\nLuowei\u00a0Zhou,\u00a0Hamid\u00a0Palangi<\/a>,\u00a0Lei Zhang<\/a>,\u00a0Houdong\u00a0Hu<\/a>,\u00a0Jason Corso,\u00a0Jianfeng\u00a0Gao<\/a>\r\n\r\nVIS525:\u00a0Leveraging Multi-view Image Sets for Unsupervised Intrinsic Image Decomposition and Highlight Separation<\/strong>\r\nRenjiao\u00a0Yi,\u00a0Ping Tan,\u00a0Stephen Lin<\/b>\r\n\r\nVIS561:\u00a0Uncertainty-aware Multi-shot Knowledge Distillation for Image-based Object Re-identification<\/strong>\r\nXin\u00a0Jin,\u00a0Cuiling\u00a0Lan<\/a>,\u00a0Wenjun Zeng<\/a>,\u00a0Zhibo\u00a0Chen,\r\n\r\nVIS4022: DGCN: Dynamic Graph Convolutional Network for Efficient Multi-Person Pose Estimation<\/strong>\r\nZhongwei\u00a0Qiu,\u00a0Kai\u00a0Qiu<\/a>,\u00a0Jianlong\u00a0Fu<\/a>,\u00a0Dongmei\u00a0Fu\r\n\r\nVIS5994:\u00a0Shallow Feature based Dense Attention Network for Crowd Counting<\/strong>\r\nYUNQI MIAO,\u00a0Zijia\u00a0Lin<\/a>,\u00a0Guiguang\u00a0Ding,\u00a0Jungong\u00a0Han\r\n

Monday, February 10<\/h2>\r\n

9:30 AM\u201310:45 AM<\/h3>\r\nTech Session 3: NLP: Generation | Sutton North\r\nOral 6824: A Dataset for Low-Resource Stylized Sequence-to-Sequence Generation<\/strong>\r\nYu Wu<\/a>,\u00a0Yunli\u00a0Wang,\u00a0Shujie\u00a0Liu<\/a>\r\n\r\nTech Session 3: NLP: Generation | Sutton North\r\nPoster 6828: Complementary Auxiliary Classifiers for Label-Conditional Text Generation<\/strong>\r\nYuan Li, Chunyuan Li<\/a>, Yizhe Zhang<\/a>, Xiujun Li<\/a>, Guoqing Zheng<\/a>, Lawrence Carin Duke, Jianfeng Gao<\/a>\r\n\r\nTech Session 3: NLP: Generation | Sutton North\r\nPoster 7682: Cross-Lingual Natural Language Generation via Pre-Training<\/strong>\r\nZewen Chi, Li Dong<\/a>, Furu Wei<\/a>, Wenhui Wang<\/a>, Xian-Ling Mao, Heyan Huang\r\n\r\nTech Session 6: ML: Optimization, Neural Architecture Search, and Others | Concourse A\r\nPoster 2073: Light Multi-segment Activation for Model Compression<\/strong>\r\nZhenhui Xu, Guolin Ke<\/a>, Jia Zhang<\/a>, Jiang Bian<\/a>, Tieyan Liu\r\n\r\nTech Session 8: Humans and AI | Clinton\r\nOral 9127: Human-Machine Collaboration for Fast Land Cover Mapping<\/strong>\r\nCaleb Robinson, Anthony Ortiz, Nikolay Malkin, Blake Elias, Andi Peng<\/a>, Dan Morris<\/a>, Bistra Dilkina, Nebojsa Jojic<\/a>\r\n\r\nTech Session 9: Games: Description Languages and NLP | Madison\r\nOral 9405: Interactive Fiction Games: A Colossal Adventure<\/strong>\r\nMatthew Hausknecht<\/a>, Prithviraj Ammanabrolu, Marc-Alexandre C\u00f4t\u00e9<\/a>, Xingdi Yuan<\/a>\r\n

11:15 AM\u201312:30 PM<\/h3>\r\nSession 3: NLP: Machine Translation | Sutton North\r\nPoster 4794: Bridging the Gap between Pre-Training and Fine-Tuning for End-to-End Speech Translation<\/strong>\r\nChengyi Wang, Yu Wu<\/a>, Shujie Liu<\/a>, Zhenglu Yang, Ming Zhou<\/a>\r\n\r\nTech Session 5: ML: Neural Nets Theory, Models and Algorithms | Murray Hill\r\nOral 9804: Differential Equation Units: Learning Functional Forms of Activation Functions from Data<\/strong>\r\nMohamadali Torkamani, Shiv Shankar, Pedram Rooshenas, Philip Wallis<\/strong>\r\n

2:00 PM\u20133:15 PM<\/h3>\r\nTech Session 4: ML: RL and Multiagent RL | Regent\r\nPoster 2312: Learning Agent Communication under Limited Bandwidth by Message Pruning<\/strong>\r\nHangyu Mao, Zhengchao Zhang, Zhen Xiao, Zhibo Gong, Yan Ni<\/a>\r\n\r\nTech Session 3: NLP: Machine Translation\/Vison: Language + Vision | Sutton North\r\nPoster 4133: Unicoder-VL: A Universal Encoder for Vision and Language by Cross-Modal Pre-Training<\/strong>\r\nGen Li, Nan Duan<\/a>, Yuejian Fang, Ming Gong<\/a>, Daxin Jiang<\/a>\r\n

3:45 PM\u20135:15 PM<\/h3>\r\nTech Session 3: NLP: Information Extraction, Retrieval and Text Mining | Sutton North\r\nOral 6492: Simultaneously Linking Entities and Extracting Relations from Biomedical Text without Mention-Level Supervision<\/strong>\r\nTrapit Bansal, Pat Verga, Neha Choudhary<\/strong>, Andrew McCallum\r\n\r\nTech Session 3: NLP: Information Extraction, Retrieval and Text Mining | Sutton North\r\nPoster 7067: Table2Analysis: Modeling and Recommendation of Common Analysis Patterns for Multi-Dimensional Data<\/strong>\r\nMengyu Zhou<\/a>, Tao Wang, Pengxin Ji, Shi Han<\/a>, Dongmei Zhang<\/a>\r\n\r\nTech Session 9: Social Networks, Influence, and Homophily | Clinton\r\nOral 5649: Adaptive Greedy Versus Non-adaptive Greedy for Influence Maximization<\/strong>\r\nWei Chen<\/a>, Binghui Peng, Grant Schoenebeck, Biaoshuai Tao\r\n\r\nTech Session 9: Social Networks, Influence, and Homophily | Clinton\r\nPoster 4623: Gradient Method for Continuous Influence Maximization with Budget-Saving Considerations<\/strong>\r\nWei Chen<\/a>, Weizhong Zhang, Haoyu Zhao\r\n\r\nTech Session 10: Reasoning under Uncertainty | Madison\r\nPoster 3579: Reliable and Efficient Anytime Skeleton Learning<\/strong>\r\nRui Ding<\/strong>, Yanzhi Liu, Jingjing Tian, Zhouyu Fu, Shi Han<\/a>, Dongmei Zhang<\/a>\r\n

7:20 PM\u20139:20 PM\u00a0| Poster Session<\/h3>\r\nAIW4623:\u00a0Gradient Method for Continuous Influence Maximization with Budget-Saving Considerations<\/strong>\r\nWei Chen<\/a>,\u00a0Weizhong\u00a0Zhang,\u00a0Haoyu\u00a0Zhao\r\n\r\nAIW7067:\u00a0Table2Analysis: Modeling and Recommendation of Common Analysis Patterns for Multi-Dimensional Data<\/strong>\r\nMengyu\u00a0Zhou<\/a>,\u00a0Tao Wang,\u00a0Pengxin\u00a0Ji,\u00a0Shi Han<\/a>,\u00a0Dongmei\u00a0Zhang<\/a>\r\n\r\nAPP5649:\u00a0Adaptive Greedy Versus Non-adaptive\u00a0Greedy for Influence Maximization<\/strong>\r\nWei Chen<\/a>,\u00a0Binghui\u00a0Peng,\u00a0Grant\u00a0Schoenebeck,\u00a0Biaoshuai\u00a0Tao\r\n\r\nHAC9127:\u00a0Human-Machine Collaboration for Fast Land Cover Mapping<\/strong>\r\nCaleb Robinson,\u00a0Anthony Ortiz,\u00a0Nikolay\u00a0Malkin,\u00a0Blake Elias,\u00a0Andi Peng<\/a>,\u00a0Dan Morris<\/a>,\u00a0Bistra\u00a0Dilkina,\u00a0Nebojsa\u00a0Jojic<\/a>\r\n\r\nML2073:\u00a0Light Multi-segment Activation for Model Compression<\/strong>\r\nZhenhui\u00a0Xu,\u00a0Guolin\u00a0Ke<\/a>,\u00a0Jia Zhang<\/a>,\u00a0Jiang Bian<\/a>,\u00a0Tieyan\u00a0Liu\r\n\r\nML2312:\u00a0Learning Agent Communication under Limited Bandwidth by Message Pruning<\/strong>\r\nHangyu\u00a0Mao,\u00a0Zhengchao\u00a0Zhang,\u00a0Zhen Xiao,\u00a0Zhibo\u00a0Gong,\u00a0Yan Ni<\/a>\r\n\r\nML9405:\u00a0Interactive Fiction Games: A Colossal Adventure<\/strong>\r\nMatthew\u00a0Hausknecht<\/a>,\u00a0Prithviraj\u00a0Ammanabrolu,\u00a0Marc-Alexandre\u00a0C\u00f4t\u00e9<\/a>,\u00a0Xingdi\u00a0Yuan<\/a>\r\n\r\nML9804:\u00a0Differential Equation Units: Learning Functional Forms of Activation Functions from Data<\/strong>\r\nMohamadali\u00a0Torkamani,\u00a0Shiv Shankar,\u00a0Pedram\u00a0Rooshenas,\u00a0Philip Wallis<\/b>\r\n\r\nNLP4794:\u00a0Bridging the Gap between Pre-Training and Fine-Tuning for End-to-End Speech Translation<\/strong>\r\nChengyi\u00a0Wang,\u00a0Yu Wu<\/a>,\u00a0Shujie\u00a0Liu<\/a>,\u00a0Zhenglu\u00a0Yang,\u00a0Ming Zhou<\/a>\r\n\r\nNLP6828:\u00a0Complementary Auxiliary Classifiers for Label-Conditional Text Generation<\/strong>\r\nYuan Li,\u00a0Chunyuan\u00a0Li<\/a>,\u00a0Yizhe\u00a0Zhang<\/a>,\u00a0Xiujun\u00a0Li<\/a>,\u00a0Guoqing\u00a0Zheng<\/a>,\u00a0Lawrence Carin Duke,\u00a0Jianfeng\u00a0Gao<\/a>\r\n\r\nNLP7682:\u00a0Cross-Lingual Natural Language Generation via Pre-Training<\/strong>\r\nZewen\u00a0Chi,\u00a0Li Dong<\/a>,\u00a0Furu\u00a0Wei<\/a>,\u00a0Wenhui\u00a0Wang<\/a>,\u00a0Xian-Ling Mao,\u00a0Heyan\u00a0Huang\r\n\r\nRU3579:\u00a0Reliable and Efficient Anytime Skeleton Learning<\/strong>\r\nRui Ding<\/strong>,\u00a0Yanzhi\u00a0Liu,\u00a0Jingjing\u00a0Tian,\u00a0Zhouyu\u00a0Fu,\u00a0Shi Han<\/a>,\u00a0Dongmei\u00a0Zhang<\/a>\r\n\r\nVIS4133:\u00a0Unicoder-VL: A Universal Encoder for Vision and Language by Cross-Modal Pre-Training<\/strong>\r\nGen Li,\u00a0Nan\u00a0Duan<\/a>,\u00a0Yuejian\u00a0Fang,\u00a0Ming Gong<\/a>,\u00a0Daxin Jiang<\/a>\r\n

Tuesday, February 11<\/h2>\r\n

9:30 AM\u201310:45 AM<\/h3>\r\nTech Session 4: ML: RL and Multiagent RL | Regent\r\nPoster 6906: Metareasoning in Modular Software Systems: On-the-Fly Configuration Using Reinforcement Learning with Rich Contextual Representations<\/strong>\r\nAditya Modi, Debadeepta Dey<\/a>, Alekh Agarwal<\/a>, Adith Swaminathan<\/a>, Besmira Nushi<\/a>, Sean Andrist<\/a>, Eric Horvitz<\/a>\r\n\r\nTech Session 4: ML: RL and Multiagent RL | Regent\r\nPoster 9280: Actor Critic Deep Reinforcement Learning for Neural Malware Control<\/strong>\r\nYu Wang<\/strong>, Jack Stokes<\/strong>, Mady Marinescu<\/strong>\r\n\r\nTech Session 5: Vision: Image Retrieval, Ranking, Recognition | Gramercy\r\nPoster 7144: Functionality Discovery and Prediction of Physical Objects<\/strong>\r\nLei Ji<\/a>, Botian Shi, Xianglin Guo, Xilin Chen\r\n

11:15 AM\u201312:30 PM<\/h3>\r\nTech Session 3: NLP: Representation Learning | Sutton South\r\nOral 3656: P-SIF: Document Embeddings Using Partition Averaging<\/strong>\r\nVivek Gupta, Ankit Saw, Pegah Nokhiz, Praneeth Netrapalli<\/a>, Piyush Rai, Partha Talukdar\r\n\r\nTech Session 3: NLP: Representation Learning | Sutton South\r\nOral 5766: TextNAS: A Neural Architecture Search Space Tailored for Text Representation<\/strong>\r\nYujing Wang, Yaming Yang, Yiren Chen, Jing Bai<\/a>, Ce Zhang, Guinan Su, Xiaoyu Kou, Yunhai Tong, Mao Yang<\/a>, Lidong Zhou<\/a>\r\n\r\nTech Session 3: NLP: Representation Learning | Sutton South\r\nPoster 7202: Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding<\/strong>\r\nOren Barkan, Noam Razin<\/a>, Itzik Malkiel<\/a>, Ori Katz<\/strong>, Avi Caciularu<\/a>, Noam Koenigstein<\/a>\r\n\r\nTech Session 4: ML: Neural Nets Theory, Models and Algorithms | Regent\r\nPoster 1536: Posterior-Guided Neural Architecture Search<\/strong>\r\nYizhou Zhou, Xiaoyan Sun<\/a>, Chong Luo<\/a>, Zheng-Jun Zha, Wenjun Zeng<\/a>\r\n\r\nTech Session 12: Mechanism Design and Fair Allocation | Morgan\r\nOral 4617: Online Second Price Auction with Semi-bandit Feedback Under the Non-Stationary Setting<\/strong>\r\nHaoyu Zhao, Wei Chen\r\n

2:00 PM\u20133:30 PM<\/h3>\r\nTech Session 2: NLP: Representation Learning, Summarization | Sutton North\r\nPoster 9252: Latent Relation Language Models<\/strong>\r\nHiroaki Hayashi, Zecong Hu, Chenyan Xiong<\/a>, Graham Neubig\r\n\r\nTech Session 3: NLP: Semantics and Summarization | Sutton South\r\nOral 3184: Graph-based Transformer with Cross-Candidate Verification for Semantic Parsing<\/strong>\r\nBo Shao, Yeyun Gong<\/strong>, Weizhen Qi, Guihong Cao<\/a>, Jianshu Ji<\/a>, Xiaola Lin\r\n\r\nTech Session 5: Vision: Human: Pose and Motion Analysis, Detection, Modeling | Gramercy\r\nPoster 2669: Semantics-Aligned Representation Learning for Person Re-identification<\/strong>\r\nXin Jin, Cuiling Lan<\/a>, Wenjun Zeng<\/a>, Guoqiang Wei, Zhibo Chen\r\n\r\nTech Session 5: Vision: Human: Pose and Motion Analysis, Detection, Modeling | Gramercy\r\nPoster 8881: Crowd Counting with Decomposed Uncertainty<\/strong>\r\nMin-hwan Oh, Peder Olsen<\/a>, Karthikeyan Natesan Ramamurthy\r\n\r\nTech Session 7: Vision: Vision + Language | Nassau\r\nPoster 1374: Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language<\/strong>\r\nSongyang Zhang, Houwen Peng<\/a>, Jianlong Fu<\/a>, Jiebo Luo\r\n\r\nTech Session 7: Vision: Vision + Language | Nassau\r\nPoster 2236: DualVD: An Adaptive Dual Encoding Model for Deep Visual Understanding in Visual Dialogue<\/strong>\r\nXiaoze Jiang, Jing Yu, Yingying Zhuang, Zengchang Qin, Yue Hu, Qi Wu, Xingxing Zhang<\/a>\r\n\r\nTech Session 7: Vision: Vision + Language | Nassau\r\nPoster 3499: What Makes A Good Story? Designing Composite Rewards for Visual Storytelling<\/strong>\r\nJunjie Hu, Yu Cheng<\/a>, Zhe Gan<\/a>, Jingjing Liu<\/a>, Jianfeng Gao<\/a>, Graham Neubig,\r\n\r\nTech Session 13: Mechanism Design | Bryant\r\nOral 1796: Preventing Arbitrage from Collusion When Eliciting Probabilities<\/strong>\r\nRupert Freeman<\/a>, David Pennock, Dominik Peters, Bo Waggoner\r\n

Wednesday, February 12<\/h2>\r\n

11:30 AM\u201312:30 PM<\/h3>\r\nTech Session 1: NLP: Language Models | Trianon\r\nOral 4504: Alternating Language Modeling for Cross-Lingual Pre-Training<\/strong>\r\nJian Yang,\u00a0Shuming\u00a0Ma<\/a>,\u00a0Dongdong\u00a0Zhang<\/a>,\u00a0ShuangZhi\u00a0Wu,\u00a0Zhoujun\u00a0Li,\u00a0Ming Zhou<\/a>\r\n\r\nTech Session 4: NLP: Semantics and Summarization | Sutton South\r\nOral 7131: Multi-level Head-wise Match and Aggregation in Transformer for Textual Sequence Matching<\/strong>\r\nShuohang\u00a0Wang<\/a>,\u00a0Yunshi\u00a0Lan,\u00a0Yi Tay,\u00a0Jing Jiang,\u00a0Jingjing\u00a0Liu<\/a>\r\n

2:00 PM\u20133:20 PM<\/h3>\r\nTech Session 1: NLP: Machine Translation, Entity Recognition and Linking | Trianon\r\nOral 2729: Fine-Tuning by Curriculum Learning for Non-Autoregressive Neural Machine Translation<\/strong>\r\nJunliang Guo, Xu Tan<\/a>, Linli Xu, Tao Qin<\/a>, Tieyan Liu, Enhong Chen\r\n\r\nTech Session 1: NLP: Machine Translation, Entity Recognition and Linking | Trianon\r\nOral 3465: Transductive Ensemble Learning for Neural Machine Translation<\/strong>\r\nYiren Wang, Lijun Wu, Yingce Xia<\/a>, Tao Qin<\/a>, Cheng Xiang Zhai, Tieyan Liu\r\n

6:30 PM\u20138:30 PM | Poster Session<\/h3>\r\nAIW7144:\u00a0Functionality Discovery and Prediction of Physical Objects<\/strong>\r\nLei Ji<\/a>,\u00a0Botian\u00a0Shi,\u00a0Xianglin\u00a0Guo,\u00a0Xilin\u00a0Chen\r\n\r\nAPP9280:\u00a0Actor Critic Deep Reinforcement Learning for Neural Malware Control<\/strong>\r\nYu Wang<\/b>,<\/b>\u00a0Jack Stokes<\/b>,<\/b>\u00a0Mady<\/b>\u00a0Marinescu<\/b>\r\n\r\nGTEP1796:\u00a0Preventing Arbitrage\u00a0from Collusion When Eliciting Probabilities<\/strong>\r\nRupert Freeman<\/a>,\u00a0David Pennock,\u00a0Dominik Peters,\u00a0Bo Waggoner\r\n\r\nML1536:\u00a0Posterior-Guided Neural Architecture Search<\/strong>\r\nYizhou\u00a0Zhou,\u00a0Xiaoyan\u00a0Sun<\/a>,\u00a0Chong Luo<\/a>,\u00a0Zheng-Jun\u00a0Zha,\u00a0Wenjun Zeng<\/a>\r\n\r\nML2669:\u00a0Semantics-Aligned Representation Learning for Person Re-identification<\/strong>\r\nXin\u00a0Jin,\u00a0Cuiling\u00a0Lan<\/a>,\u00a0Wenjun Zeng<\/a>,\u00a0Guoqiang\u00a0Wei,\u00a0Zhibo\u00a0Chen\r\n\r\nML3465:\u00a0Transductive\u00a0Ensemble Learning for Neural Machine Translation<\/strong>\r\nYiren\u00a0Wang,\u00a0Lijun Wu,\u00a0Yingce\u00a0Xia<\/a>,\u00a0Tao Qin<\/a>,\u00a0Cheng Xiang\u00a0Zhai,\u00a0Tieyan\u00a0Liu\r\n\r\nML4617:\u00a0Online Second Price Auction with Semi-bandit Feedback Under the Non-Stationary Setting<\/strong>\r\nHaoyu\u00a0Zhao,\u00a0Wei Chen<\/a>\r\n\r\nML6906:\u00a0Metareasoning\u00a0in Modular Software Systems: On-the-Fly Configuration Using Reinforcement Learning with Rich Contextual Representations<\/strong>\r\nAditya Modi,\u00a0Debadeepta\u00a0Dey<\/a>,\u00a0Alekh\u00a0Agarwal<\/a>,\u00a0Adith\u00a0Swaminathan<\/a>,\u00a0Besmira Nushi<\/a>,\u00a0Sean Andrist<\/a>,\u00a0Eric Horvitz<\/a>\r\n\r\nNLP2729:\u00a0Fine-Tuning by Curriculum Learning for Non-Autoregressive Neural Machine Translation<\/strong>\r\nJunliang\u00a0Guo,\u00a0Xu Tan<\/a>,\u00a0Linli\u00a0Xu,\u00a0Tao Qin<\/a>,\u00a0Tieyan\u00a0Liu,\u00a0Enhong\u00a0Chen\r\n\r\nNLP3184:\u00a0Graph-based Transformer with Cross-Candidate Verification for Semantic Parsing<\/strong>\r\nBo Shao,\u00a0Yeyun<\/b>\u00a0Gong<\/b>,\u00a0Weizhen\u00a0Qi,\u00a0Guihong\u00a0Cao<\/a>,\u00a0Jianshu\u00a0Ji<\/a>,\u00a0Xiaola\u00a0Lin\r\n\r\nNLP3656:\u00a0P-SIF: Document Embeddings Using Partition Averaging<\/strong>\r\nVivek Gupta,\u00a0Ankit Saw,\u00a0Pegah\u00a0Nokhiz,\u00a0Praneeth\u00a0Netrapalli<\/a>,\u00a0Piyush Rai,\u00a0Partha\u00a0Talukdar\r\n\r\nNLP5766:\u00a0TextNAS: A Neural Architecture Search Space\u00a0Tailored for Text Representation<\/strong>\r\nYujing\u00a0Wang,\u00a0Yaming\u00a0Yang,\u00a0Yiren\u00a0Chen,\u00a0Jing Bai<\/a>,\u00a0Ce Zhang,\u00a0Guinan\u00a0Su,\u00a0Xiaoyu\u00a0Kou,\u00a0Yunhai\u00a0Tong,\u00a0Mao Yang<\/a>,\u00a0Lidong\u00a0Zhou<\/a>\r\n\r\nNLP7202:\u00a0Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding<\/strong>\r\nOren\u00a0Barkan,\u00a0Noam\u00a0Razin<\/a>,\u00a0Itzik\u00a0Malkiel<\/a>,\u00a0Ori Katz<\/strong>,\u00a0Avi\u00a0Caciularu<\/a>,\u00a0Noam\u00a0Koenigstein<\/a>\r\n\r\nNLP9252:\u00a0Latent Relation Language Models<\/strong>\r\nHiroaki Hayashi,\u00a0Zecong\u00a0Hu,\u00a0Chenyan\u00a0Xiong<\/a>,\u00a0Graham\u00a0Neubig\r\n\r\nVIS1374:\u00a0Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language<\/strong>\r\nSongyang\u00a0Zhang,\u00a0Houwen\u00a0Peng<\/a>,\u00a0Jianlong\u00a0Fu<\/a>,\u00a0Jiebo\u00a0Luo\r\n\r\nVIS2236:\u00a0DualVD: An Adaptive Dual Encoding Model for Deep Visual Understanding in Visual Dialogue<\/strong>\r\nXiaoze\u00a0Jiang,\u00a0Jing Yu,\u00a0Yingying Zhuang,\u00a0Zengchang\u00a0Qin,\u00a0Yue Hu,\u00a0Qi Wu,\u00a0Xingxing\u00a0Zhang<\/a>\r\n\r\nVIS3499:\u00a0What Makes A Good Story? Designing Composite Rewards for Visual Storytelling<\/strong>\r\nJunjie\u00a0Hu,\u00a0Yu Cheng<\/a>,\u00a0Zhe\u00a0Gan<\/a>,\u00a0Jingjing\u00a0Liu<\/a>,\u00a0Jianfeng\u00a0Gao<\/a>,\u00a0Graham\u00a0Neubig,\r\n\r\nVIS8881:\u00a0Crowd Counting with Decomposed Uncertainty<\/strong>\r\nMin-hwan\u00a0Oh,\u00a0Peder\u00a0Olsen<\/a>,\u00a0Karthikeyan Natesan Ramamurthy"},{"id":2,"name":"Workshops","content":"

Friday, February 7<\/h2>\r\nW1: Affective Content Analysis (AffCon\u00a02020): Interactive Affective Response<\/a>\r\nInvited Speaker:\u00a0Daniel McDuff<\/a>\r\n\r\nW5:\u00a0Artificial Intelligence of Things (AIoT)<\/a>\r\nProgram Co-Chairs:\u00a0Jian Zhang<\/b>\r\nSteering Committee Member:\u00a0Victor\u00a0Bahl<\/a>\r\nKeynote Speaker:\u00a0Ranveer Chandra<\/a>\r\nAccepted Paper: In-Car Cognition with Edge Artificial Intelligence Accelerators\r\nMing-<\/b>Chih<\/b>\u00a0Lin,\u00a0<\/b>Sio<\/b>-Sun Wong, Yu-<\/b>Kwen<\/b>\u00a0Hsu,\u00a0<\/b>Ti<\/b>-Hua Yang, Pi-Sui Hsu, He-Wei Lee, Wei-Chen Tsai, Hsiu-Tzu Wu, Sung-Lin Yeh<\/b>\r\n\r\nW7:\u00a0Cloud Intelligence: AI\/ML for Efficient and Manageable Cloud Services<\/a>\r\nProgram Chair:\u00a0Jian Zhang<\/b>\r\nSteering Committee Members:Ricardo\u00a0Bianchini<\/a>, Marcus\u00a0Fontoura<\/a>,\u00a0Dongmei\u00a0Zhang<\/a>\r\nKeynote Speaker:\u00a0Marcus\u00a0Fontoura<\/a>\r\nInvited Speaker:\u00a0Dongmei\u00a0Zhang<\/a>\r\n\r\nW14: Workshop on Intelligent Process Automation<\/a>\r\nKeynote Speaker: Sumit Gulwani<\/a>\r\n\r\nW22: Reproducibility in AI (RAI 2020) \u2014 Future Direction and Reproducibility Challenge<\/a>\r\nInvited Speaker: Vani Mandava<\/a>\r\n

Saturday, February 8<\/h2>\r\nW9:\u00a0The Eighth Dialog System Technology Challenge (DSTC8)<\/a>\r\nWorkshop Chair:\u00a0Michel Galley<\/a>\r\nTrack 1 Co-organizers: Jinchao Li<\/a>, Baolin Peng<\/a>, Jianfeng Gao<\/a>, Hannes Schulz<\/a>, Adam Atkinson<\/a>, Mahmoud Adada<\/a>\r\n\r\nW11: Evaluating Evaluation of AI Systems<\/a>\r\nProgram Committee Members:\u00a0Sid Suri<\/a>, Omar Alonso<\/a>"},{"id":3,"name":"Career opportunities","content":"

Azure Global<\/h3>\r\nResearch Intern<\/a>\r\nSenior Software Engineer<\/a>\r\nPrincipal SDE<\/a>\r\n

Azure Production Infrastructure Engineering Team<\/h3>\r\nSenior Software Engineer<\/a>\r\nData and Applied Scientist II<\/a>\r\n

Bing<\/h3>\r\nSenior Applied Scientist<\/a>\r\nSenior Applied Scientist<\/a>\r\n

Security and Compliance Loonshot Research Team<\/h3>\r\nData Engineer<\/a>\r\nSenior Security Engineer<\/a>\r\nSenior Research Program Manager<\/a>\r\nPrincipal Applied Researcher<\/a>\r\nPrincipal Applied Researcher<\/a>\r\nApplied Researcher II<\/a>\r\nApplied Researcher II<\/a>\r\n

Full-Time Opportunities for PhD Students or Recent Graduates<\/h3>\r\nAI Researcher<\/a>\r\nComputer Vision Engineer<\/a>\r\nNatural Language Processing Software Development Engineer<\/a>\r\nMachine Learning Engineer<\/a>\r\nProgram Manager & Software Engineer<\/a>"}],"msr_startdate":"2020-02-07","msr_enddate":"2020-02-12","msr_event_time":"","msr_location":"New York, NY","msr_event_link":"","msr_event_recording_link":"","msr_startdate_formatted":"February 7, 2020","msr_register_text":"Watch now","msr_cta_link":"","msr_cta_text":"","msr_cta_bi_name":"","featured_image_thumbnail":"\"skyline","event_excerpt":"Microsoft is proud to be a sponsor of the Thirty-Fourth AAAI Conference on Artificial Intelligence. Over 50 of our researchers will be at AAAI - stop by our booth to chat with our experts, see demos of our latest research and find out about career opportunities with Microsoft.","msr_research_lab":[],"related-researchers":[],"msr_impact_theme":[],"related-academic-programs":[],"related-groups":[],"related-projects":[],"related-opportunities":[],"related-publications":[630045],"related-videos":[],"related-posts":[610734,631245,635229],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/633696","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-event"}],"version-history":[{"count":7,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/633696\/revisions"}],"predecessor-version":[{"id":1146997,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/633696\/revisions\/1146997"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/634065"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=633696"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=633696"},{"taxonomy":"msr-region","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-region?post=633696"},{"taxonomy":"msr-event-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event-type?post=633696"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=633696"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=633696"},{"taxonomy":"msr-program-audience","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-program-audience?post=633696"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=633696"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=633696"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}