6.5k citations · 10.4k across the 118 of their papers we have counts for
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Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior
Shashank Subramanian, Peter Harrington, Kurt Keutzer +4
Pre-trained machine learning (ML) models have shown great performance for a wide range of applications, in particular in natural language processing (NLP) and computer vision (CV).…
Differentiable NAS Framework and Application to Ads CTR Prediction
Ravi Krishna, Aravind Kalaiah, Bichen Wu +4
Neural architecture search (NAS) methods aim to automatically find the optimal deep neural network (DNN) architecture as measured by a given objective function, typically some comb…
Invariant Information Bottleneck for Domain Generalization
Bo Li, Yifei Shen, Yezhen Wang +6
Invariant risk minimization (IRM) has recently emerged as a promising alternative for domain generalization. Nevertheless, the loss function is difficult to optimize for nonlinear…
Improving Context-Based Meta-Reinforcement Learning with Self-Supervised Trajectory Contrastive Learning
Bernie Wang, Simon Xu, Kurt Keutzer +2
Meta-reinforcement learning typically requires orders of magnitude more samples than single task reinforcement learning methods. This is because meta-training needs to deal with mo…
BeBold: Exploration Beyond the Boundary of Explored Regions
Tianjun Zhang, Huazhe Xu, Xiaolong Wang +4
Efficient exploration under sparse rewards remains a key challenge in deep reinforcement learning. To guide exploration, previous work makes extensive use of intrinsic reward (IR).…
Multi-Agent Collaboration via Reward Attribution Decomposition
Tianjun Zhang, Huazhe Xu, Xiaolong Wang +4
Recent advances in multi-agent reinforcement learning (MARL) have achieved super-human performance in games like Quake 3 and Dota 2. Unfortunately, these techniques require orders-…