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ICML

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ICML

2024

Align Your Steps: Optimizing Sampling Schedules in Diffusion Models

Amirmojtaba Sabour, Sanja Fidler, Karsten Kreis

ICML

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

ICML

2023

Bayesian Reparameterization of Reward-Conditioned Reinforcement Learning with Energy-based Models

Wenhao Ding, Gerry Che, Ding Zhao, Marco Pavone

ICML

Global Context Vision Transformers

Ali Hatamizadeh, Hongxu Danny Yin, Greg Heinrich, Jan Kautz, Pavlo Molchanov

ICML

Equivariant Architectures for Learning in Deep Weight Spaces

Aviv Navon, Aviv Shamsian, Idan Achituve, Ethan Fetaya, Gal Chechik, Haggai Maron

ICML

2022

Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training

Charbel Sakr, Steve Dai, Rangharajan Venkatesan, Brian Zimmer, Brucek Khailany, William Dally

ICML

Diffusion Models for Adversarial Purification

Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, Anima Anandkumar

ICML

Optimizing tensor network contraction using reinforcement learning

Eli Meirom, Haggai Maron, Shie Mannor, Gal Chechik

ICML

2021

From local structures to size generalization in graph neural networks

Gilad Yehudai, Ethan Fetaya, Eli Meirom, Gal Chechik, Haggai Maron

ICML

Controlling graph dynamics with reinforcement learning and graph neural networks

Eli Meirom, Haggai Maron, Shie Mannor, Gal Chechik

ICML

Graph Positional Encoding via Random Feature Propagation

Moshe Eliasof, Fabrizio Frasca, Beatrice Bevilacqua, Eran Treister, Gal Chechik, Haggai Maron

ICML