View of Vancouver and the Vancouver Convention Center

December 8, 2019 – December 14, 2019

Microsoft at NeurIPS 2019

Location: Vancouver, Canada

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Come by our booth (#301) to see demos of our latest research and meet with our team! See schedule below:

Monday, December 9

Time	Demos	Meet Our Team
10:30 AM–11:15 AM	Responsible AI Infer.NET	Andrew Fitzgibbon – Vision Anna Mitenkova – RL
1:15 PM–2:00 PM	BERT on Azure ML Distributional Reward Decomposition for Reinforcement Learning	Ali Zaidi – NLP, Stats Kamil Ciosek – RL Shawn Jain – AI Residency
2:00 PM–2:45 PM	Metalearned Neural Memory Fully Parameterized Quantile Function for Distributional Reinforcement Learning	Harm van Seijen – RL Ravi Pandya – Health
4:45 PM–5:00 PM	BERT on Azure ML Icebreaker: Efficient Information Acquisition with Active Learning
6:30 PM–7:30 PM		Alex Polozov – Program synthesis, NL2Code Jan Stühmer – Generative Models, Vision Keiji Kanazawa – Azure Machine Learning Marc Brockschmidt – GNNs
6:35 PM–8:30 PM	IPU-Accelerated Medical Imaging on Microsoft Azure

Tuesday, December 10

Time	Demos	Meet Our Team
9:20 AM–10:05 AM	Responsible AI: interpretability & fairness in machine learning BERT on Azure ML	Abhishek Rao – Text classification Ehi Nosakhare – ML or healthcare/interpretability Govert Verkes – Vision John-Mark Agosta – Azure Yi Li – Knowledge graph
12:45 PM–1:30 PM	Responsible AI: interpretability & fairness in machine learning Efficient Forward Neural Architecture Search	Andrew Fitzgibbon – Vision Danielle Belgrave – AI for Healthcare Kaushal Paneri – Causal Inference Michael Revow – Real time communication, Video
1:30 PM–2:15 PM	Responsible AI: interpretability & fairness in machine learning BERT on Azure ML	Anna Mitenkova – RL Cassandra Oduola – Deep Learning, CV, HCI Isabel Chien – AI for healthcare Raluca Georgescu – RL
3:25 PM–4:10 PM	Responsible AI: interpretability & fairness in machine learning Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers	Akshay Krishnamurthy – RL Theory Samira Shabanian – Generative models, FATE Marc Brockschmidt – GNNs Kaushal Paneri – Causal Inference

Wednesday, December 11

Time	Demos	Meet Our Team
9:20 AM–10:05 AM	High performance inferencing for interoperable ONNX models Microsoft Rocket Video Analytics Platform	Andrew Fitzgibbon – Vision Anuj Bhatia – Azure Javier Alvarez – AI for healthcare Yuanchao Shu – Video and Systems
12:45 PM–1:30 PM	High performance inferencing for interoperable ONNX models Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning	Forough Poursabzi-Sangdeh – FATE, CSS Lindsey Allen – AI Platform Siyu Yang – AI for Good Yi Li – Knowledge graph Yixi Xu – AI for Good
1:30 PM–2:15 PM	High performance inferencing for interoperable ONNX models FastSpeech: Fast, Robust and Controllable Text to Speech	Danielle Belgrave – AI for Healthcare Judy Shen – AI Residency Lindsey Allen – AI Platform Marc Brockschmidt – GNNs Raluca Georgescu – RL
3:05 PM–3:50 PM	Responsible AI: interpretability & fairness in machine learning Infer.NET	Alex Polozov – Program synthesis, NL2Code Allison Hegel – AI Residency Ehi Nosakhare – MAIDAP/ ML for health/ Interpretability Forough Poursabzi-Sangdeh – FATE, CSS Lindsey Allen – AI Platform