Liquid at NeurIPS 2023

The NeurIPS conference stands at the forefront of AI research. This year, we are thrilled to share that our research team members have contributed to the following papers*.

Conference main track

• Gigastep - One Billion Steps per Second Multi-agent Reinforcement Learning Mathias Lechner, Lianhao Yin, Tim Seyde, Tsun-Hsuan Wang, Wei Xiao, Ramin Hasani, Joshua Rountree, Daniela Rus
• On the Size and Approximation Error of Distilled Datasets Alaa Maalouf, Murad Tukan, Noel Loo, Ramin Hasani, Mathias Lechner, Daniela Rus‍
• Compositional Policy Learning in Stochastic Control Systems with Formal Guarantees Đorđe Žikelić, Mathias Lechner, Abhinav Verma, Krishnendu Chatterjee, Thomas A Henzinger‍
• Convolutional State Space Models for Long-Range Spatiotemporal Modeling Jimmy T.H. Smith, Shalini De Mello, Jan Kautz, Scott Linderman, Wonmin Byeon‍
• Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions Stefano Massaroli, Michael Poli, Daniel Y Fu, Hermann Kumbong, Rom Nishijima Parnichkun, David W. Romero, Aman Timalsina, Quinn McIntyre, Beidi Chen, Atri Rudra, Ce Zhang, Christopher Re, Stefano Ermon, Yoshua Bengio‍
• HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution Eric Nguyen, Michael Poli, Marjan Faizi, Armin W Thomas, Michael Wornow, Callum Birch-Sykes, Stefano Massaroli, Aman Patel, Clayton M. Rabideau, Yoshua Bengio, Stefano Ermon, Christopher Re, Stephen Baccus‍
• Improving *day-ahead* Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context Oussama Boussif, Ghait Boukachab, Dan Assouline, Stefano Massaroli, Tianle Yuan, Loubna Benabbou, Yoshua Bengio‍
• Learning Efficient Surrogate Dynamic Models with Graph Spline Networks Chuanbo Hua, Federico Berto, Michael Poli, Stefano Massaroli, Jinkyoo Park‍
• DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models Tsun-Hsuan Wang, Juntian Zheng, Pingchuan Ma, Yilun Du, Byungchul Kim, Andrew Everett Spielberg, Joshua B. Tenenbaum, Chuang Gan, Daniela Rus
• ‍Expressive Sign Equivariant Networks for Spectral Geometric Learning Derek Lim, Joshua Robinson, Stefanie Jegelka, Haggai Maron

Workshop papers

• Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models Tsun-Hsuan Wang, Alaa Maalouf, Wei Xiao, Yutong Ban, Alexander Amini, Guy Rosman, Sertac Karaman, Daniela Rus‍‍
• ‍‍How Structured Data Guides Feature Learning: A Case Study of the Parity Problem Atsushi Nitanda, Kazusato Oko, Taiji Suzuki, Denny Wu‍
• Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control Neehal Tumma, Mathias Lechner, Noel Loo, Ramin Hasani, Daniela Rus‍
• Exploring Modern Evolution Strategies in Portfolio Optimization at OPT

_{* Disclaimer: Liquid research team members (highlighted in bold and italic) have co-authored these papers at their respective organizations independently of the work that they do at Liquid AI, Inc.}