Get our free extension to see links to code for papers anywhere online!

Add to Chrome

Add to Firefox

Picture for Christopher Ré

Christopher Ré

Effectively Modeling Time Series with Simple Discrete State Spaces

Mar 16, 2023
Michael Zhang, Khaled K. Saab, Michael Poli, Tri Dao, Karan Goel, Christopher Ré

Figure 1 for Effectively Modeling Time Series with Simple Discrete State Spaces

Figure 2 for Effectively Modeling Time Series with Simple Discrete State Spaces

Figure 3 for Effectively Modeling Time Series with Simple Discrete State Spaces

Figure 4 for Effectively Modeling Time Series with Simple Discrete State Spaces

* 45 pages, 8 figures, 20 tables, ICLR 2023

Access Paper or Ask Questions

Hyena Hierarchy: Towards Larger Convolutional Language Models

Mar 06, 2023
Michael Poli, Stefano Massaroli, Eric Nguyen, Daniel Y. Fu, Tri Dao, Stephen Baccus, Yoshua Bengio, Stefano Ermon, Christopher Ré

Figure 1 for Hyena Hierarchy: Towards Larger Convolutional Language Models

Figure 2 for Hyena Hierarchy: Towards Larger Convolutional Language Models

Figure 3 for Hyena Hierarchy: Towards Larger Convolutional Language Models

Figure 4 for Hyena Hierarchy: Towards Larger Convolutional Language Models

* Additional results (PG-19, LAMBADA)

Access Paper or Ask Questions

Collage Diffusion

Mar 01, 2023
Vishnu Sarukkai, Linden Li, Arden Ma, Christopher Ré, Kayvon Fatahalian

Figure 1 for Collage Diffusion

Figure 2 for Collage Diffusion

Figure 3 for Collage Diffusion

Figure 4 for Collage Diffusion

* 26 pages, 20 figures

Access Paper or Ask Questions

Transform Once: Efficient Operator Learning in Frequency Domain

Nov 26, 2022
Michael Poli, Stefano Massaroli, Federico Berto, Jinykoo Park, Tri Dao, Christopher Ré, Stefano Ermon

Figure 1 for Transform Once: Efficient Operator Learning in Frequency Domain

Figure 2 for Transform Once: Efficient Operator Learning in Frequency Domain

Figure 3 for Transform Once: Efficient Operator Learning in Frequency Domain

Figure 4 for Transform Once: Efficient Operator Learning in Frequency Domain

* Published at the 36th Conference on Neural Information Processing Systems (NeurIPS 2022)

Access Paper or Ask Questions

Holistic Evaluation of Language Models

Nov 16, 2022
Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel Orr, Lucia Zheng, Mert Yuksekgonul, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, Yuta Koreeda

Figure 1 for Holistic Evaluation of Language Models

Figure 2 for Holistic Evaluation of Language Models

Figure 3 for Holistic Evaluation of Language Models

Figure 4 for Holistic Evaluation of Language Models

* Authored by the Center for Research on Foundation Models (CRFM) at the Stanford Institute for Human-Centered Artificial Intelligence (HAI). Project page: https://crfm.stanford.edu/helm/v1.0

Access Paper or Ask Questions