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Found 5 papers, 4 papers with code
Explore to Generalize in Zero-Shot RL
Our insight is that learning a policy that effectively $\textit{explores}$ the domain is harder to memorize than a policy that maximizes reward for a specific task, and therefore we expect such learned behavior to generalize well; we indeed demonstrate this empirically on several domains that are difficult for invariance-based approaches.
Regularization Guarantees Generalization in Bayesian Reinforcement Learning through Algorithmic Stability
In the Bayesian reinforcement learning (RL) setting, a prior distribution over the unknown problem parameters -- the rewards and transitions -- is assumed, and a policy that optimizes the (posterior) expected return is sought.
Deep Residual Flow for Out of Distribution Detection
Specifically, we demonstrate the effectiveness of our method in ResNet and DenseNet architectures trained on various image datasets.
A Local Block Coordinate Descent Algorithm for the CSC Model
The Convolutional Sparse Coding (CSC) model has recently gained considerable traction in the signal and image processing communities.
A Local Block Coordinate Descent Algorithm for the Convolutional Sparse Coding Model
The Convolutional Sparse Coding (CSC) model has recently gained considerable traction in the signal and image processing communities.
