Publications

Our publications provide insight into some of our leading-edge research.

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2025

GenMol: A Drug Discovery Generalist with Discrete Diffusion
Seul Lee, Karsten Kreis, Srimukh Prasad Veccham, Meng Liu, Danny Reidenbach, Yuxing Peng, Saee Paliwal, Weili Nie, Arash Vahdat
Efficient Molecular Conformer Generation with SO(3)-Averaged Flow Matching and Reflow
Zhonglin Cao, Mario Geiger, Allan Dos Santos Costa, Danny Reidenbach, Karsten Kreis, Tomas Geffner, Franco Pellegrini, Guoqing Zhou, Emine Kucukbenli
SLIM: One-shot Quantization and Sparsity with Low-rank Approximation for LLM Weight Compression
Mohammad Mozaffari , Amir Yazdanbakhsh, Maryam Mehri Dehnavi
Score Distillation Sampling for Audio: Source Separation, Synthesis, and Beyond
Jessie Richter-Powell, Antonio Torralba, Jonathan Lorraine
Multi-student Diffusion Distillation for Better One-step Generators
Yanke Song, Jonathan Lorraine, Weili Nie, Karsten Kreis, James Lucas
SoftTreeMax: Policy Gradient via tree expansion
Gal Dalal, Assaf Hallak, Gugan Thoppe, Shie Mannor, Gal Chechik

2024

Align Your Steps: Optimizing Sampling Schedules in Diffusion Models
Amirmojtaba Sabour, Sanja Fidler, Karsten Kreis
DoRA: Weight-Decomposed Low-Rank Adaptation
Shih-Yang Liu, Chien-Yi Wang, Hongxu Danny Yin, Pavlo Molchanov, Frank Wang, Kwang-Ting Cheng, Min-Hung Chen
DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents
Yilun Xu, Gabriele Corso, Tommi Jaakkola, Arash Vahdat, Karsten Kreis
Neural operators with localized integral and differential kernels
Miguel Liu-Schiaffini, Julius Berner, Boris Bonev, Thorsten Kurth, Kamyar Azizzadenesheli, Anima Anandkumar

2023

Bayesian Reparameterization of Reward-Conditioned Reinforcement Learning with Energy-based Models
Wenhao Ding, Gerry Che, Ding Zhao, Marco Pavone
Global Context Vision Transformers
Ali Hatamizadeh, Hongxu Danny Yin, Greg Heinrich, Jan Kautz, Pavlo Molchanov
Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere
Boris Bonev, Thorsten Kurth, Christian Hundt, Jaideep Pathak, Maximilian Baust, Karthik Kashinath, Anima Anandkumar
Equivariant Architectures for Learning in Deep Weight Spaces
Aviv Navon, Aviv Shamsian, Idan Achituve, Ethan Fetaya, Gal Chechik, Haggai Maron

2022

Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training
Charbel Sakr, Steve Dai, Rangharajan Venkatesan, Brian Zimmer, Brucek Khailany, William Dally
Diffusion Models for Adversarial Purification
Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, Anima Anandkumar
Optimizing tensor network contraction using reinforcement learning
Eli Meirom, Haggai Maron, Shie Mannor, Gal Chechik

2021

From local structures to size generalization in graph neural networks
Gilad Yehudai, Ethan Fetaya, Eli Meirom, Gal Chechik, Haggai Maron
Controlling graph dynamics with reinforcement learning and graph neural networks
Eli Meirom, Haggai Maron, Shie Mannor, Gal Chechik
Graph Positional Encoding via Random Feature Propagation
Moshe Eliasof, Fabrizio Frasca, Beatrice Bevilacqua, Eran Treister, Gal Chechik, Haggai Maron