242 citations · 1.7k across the 67 of their papers we have counts for
11 papers · 1 filter
Simulation Intelligence: Towards a New Generation of Scientific Methods
Alexander Lavin, David Krakauer, Hector Zenil +21
The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of comput…
Adversarial Skill Chaining for Long-Horizon Robot Manipulation via Terminal State Regularization
Youngwoon Lee, Joseph J. Lim, Anima Anandkumar +1
Skill chaining is a promising approach for synthesizing complex behaviors by sequentially combining previously learned skills. Yet, a naive composition of skills fails when a polic…
Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers
John Guibas, Morteza Mardani, Zongyi Li +3
Vision transformers have delivered tremendous success in representation learning. This is primarily due to effective token mixing through self attention. However, this scales quadr…
Reinforcement Learning in Factored Action Spaces using Tensor Decompositions
Anuj Mahajan, Mikayel Samvelyan, Lei Mao +6
We present an extended abstract for the previously published work TESSERACT [Mahajan et al., 2021], which proposes a novel solution for Reinforcement Learning (RL) in large, factor…
OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation
Josiah Wong, Viktor Makoviychuk, Anima Anandkumar +1
Learning performant robot manipulation policies can be challenging due to high-dimensional continuous actions and complex physics-based dynamics. This can be alleviated through int…
AugMax: Adversarial Composition of Random Augmentations for Robust Training
Haotao Wang, Chaowei Xiao, Jean Kossaifi +3
Data augmentation is a simple yet effective way to improve the robustness of deep neural networks (DNNs). Diversity and hardness are two complementary dimensions of data augmentati…