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Published Date

Publication

Chimera: Accurate retrosynthesis prediction by ensembling models with diverse inductive biases

Krzysztof Maziarz, Guoqing Liu, Hubert Misztela, Aleksei Kornev, Piotr Gaiński, Holger Hoefling, Mike Fortunato, Rishi Gupta, Marwin Segler

December 2024

Video Project

Publication

MoE-CAP: Benchmarking Cost, Accuracy and Performance of Sparse Mixture-of-Experts Systems

Yao Fu, Yinsicheng Jiang, Yeqi Huang, Ping Nie, Zhan Lu, Leyang Xue, Congjie He, Man-Kit Sit, Jilong Xue, Li Dong, Ziming Miao, Kai Zou, E. Ponti, Luo Mai

NeurIPS 2025 | December 2024

Publication

ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer

Shun Zheng, Xumeng Wen, Jiang Bian

2024 Neural Information Processing Systems | December 2024

Publication

MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention

Huiqiang Jiang, Yucheng Li, Chengruidong Zhang, Qianhui Wu, Xufang Luo, Surin Ahn, Zhenhua Han, Amir H. Abdi, Dongsheng Li, Chin-Yew Lin, Yuqing Yang, Lili Qiu

NeurIPS 2024 | December 2024

spotlight

Github Project

Publication

Machine Unlearning Doesn’t Do What You Think: Lessons for Generative AI Policy, Research, and Practice (TR)

A. Feder Cooper, Christopher A. Choquette-Choo, Miranda Bogen, Matthew Jagielski, Katja Filippova, Ken Ziyu Liu, Alexandra Chouldechova, Jamie Hayes, Yangsibo Huang, Niloofar Mireshghallah, Ilia Shumailov, Eleni Triantafillou, Peter Kairouz, Nicole Mitchell, Percy Liang, Daniel E. Ho, Yejin Choi, Sanmi Koyejo, Fernando Delgado, James Grimmelmann, Vitaly Shmatikov, Christopher De Sa, Solon Barocas, Amy Cyphert, Mark A. Lemley, danah boyd, Jennifer Wortman Vaughan, M. Brundage, David Bau, Seth Neel, Abigail Z. Jacobs, Andreas Terzis, Hanna Wallach, Nicolas Papernot, Katherine Lee

MSR-TR-2024-61 | December 2024

Published by Microsoft

Publication

Machine Unlearning Doesn’t Do What You Think: Lessons for Generative AI Policy, Research, and Practice

A. F. Cooper, Christopher A. Choquette-Choo, Miranda Bogen, Matthew Jagielski, Katja Filippova, Ken Ziyu Liu, Alex Chouldechova, Jamie Hayes, Yangsibo Huang, Niloofar Mireshghallah, Ilia Shumailov, Eleni Triantafillou, Peter Kairouz, N. Mitchell, Percy Liang, Daniel E. Ho, Yejin Choi, Sanmi Koyejo, Fernando Delgado, James Grimmelmann, Vitaly Shmatikov, Christopher De Sa, Solon Barocas, A. Cyphert, Mark A. Lemley, danah boyd, Jennifer Wortman Vaughan, M. Brundage, David Bau, Seth Neel, Abigail Z. Jacobs, Andreas Terzis, Hanna Wallach, Nicolas Papernot, Katherine Lee

NeurIPS 2025 | December 2024

Podcast

Abstracts: NeurIPS 2024 with Weizhu Chen

December 6, 2024

Next-token prediction trains a language model on all tokens in a sequence. VP Weizhu Chen discusses his team’s 2024 NeurIPS paper on how distinguishing between useful and “noisy” tokens in pretraining can improve token efficiency…

Tool

HeurAgenix

HeurAgenix is a novel framework based on LLM, designed to generate, evolve, evaluate, and select heuristic algorithms for solving combinatorial optimization problems. It leverages the power of large language models to autonomously handle various optimization…

GitHub

Podcast

Abstracts: NeurIPS 2024 with Dylan Foster

December 6, 2024

Can existing algorithms designed for simple reinforcement learning problems be used to solve more complex RL problems? Researcher Dylan Foster discusses the modular approach he and his coauthors explored in their 2024 NeurIPS paper on…

Podcast

Abstracts: NeurIPS 2024 with Pranjal Chitale

December 6, 2024

Pranjal Chitale discusses the 2024 NeurIPS work CVQA. Spanning 31 languages and the cultures of 30 countries, this VQA benchmark was created with native speakers and cultural experts to evaluate model performance across diverse linguistic…

Artificial Intelligence

Highlights