29 citations · 35 across the 4 of their papers we have counts for
4 papers · 1 filter
Estimation of Concept Explanations Should be Uncertainty Aware
Vihari Piratla, Juyeon Heo, Katherine M. Collins +2
Model explanations can be valuable for interpreting and debugging predictive models. We study a specific kind called Concept Explanations, where the goal is to interpret a model us…
Unlocking Pixels for Reinforcement Learning via Implicit Attention
Krzysztof Marcin Choromanski, Deepali Jain, Wenhao Yu +9
There has recently been significant interest in training reinforcement learning (RL) agents in vision-based environments. This poses many challenges, such as high dimensionality an…
Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers
Krzysztof Choromanski, Valerii Likhosherstov, David Dohan +8
Transformer models have achieved state-of-the-art results across a diverse range of domains. However, concern over the cost of training the attention mechanism to learn complex dep…
Orthogonal Over-Parameterized Training
Weiyang Liu, Rongmei Lin, Zhen Liu +5
The inductive bias of a neural network is largely determined by the architecture and the training algorithm. To achieve good generalization, how to effectively train a neural netwo…