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Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings
Recent advances in off-policy deep reinforcement learning (RL) have led ...
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State Entropy Maximization with Random Encoders for Efficient Exploration
Recent exploration methods have proven to be a recipe for improving samp...
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MASKER: Masked Keyword Regularization for Reliable Text Classification
Pre-trained language models have achieved state-of-the-art accuracies on...
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Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in First-person Simulated 3D Environments
First-person object-interaction tasks in high-fidelity, 3D, simulated en...
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Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning
Model-based reinforcement learning (RL) has shown great potential in var...
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Decoupling Representation Learning from Reinforcement Learning
In an effort to overcome limitations of reward-driven feature learning i...
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Dynamics Generalization via Information Bottleneck in Deep Reinforcement Learning
Despite the significant progress of deep reinforcement learning (RL) in ...
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Learning to Sample with Local and Global Contexts in Experience Replay Buffer
Experience replay, which enables the agents to remember and reuse experi...
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SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning
Model-free deep reinforcement learning (RL) has been successful in a ran...
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Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning
Model-based reinforcement learning (RL) enjoys several benefits, such as...
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Reinforcement Learning with Augmented Data
Learning from visual observations is a fundamental yet challenging probl...
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Regularizing Class-wise Predictions via Self-knowledge Distillation
Deep neural networks with millions of parameters may suffer from poor ge...
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A Simple Randomization Technique for Generalization in Deep Reinforcement Learning
Deep reinforcement learning (RL) agents often fail to generalize to unse...
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Incremental Learning with Unlabeled Data in the Wild
Deep neural networks are known to suffer from catastrophic forgetting in...
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Robust Inference via Generative Classifiers for Handling Noisy Labels
Large-scale datasets may contain significant proportions of noisy (incor...
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Using Pre-Training Can Improve Model Robustness and Uncertainty
Tuning a pre-trained network is commonly thought to improve data efficie...
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A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
Detecting test samples drawn sufficiently far away from the training dis...
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Hierarchical Novelty Detection for Visual Object Recognition
Deep neural networks have achieved impressive success in large-scale vis...
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Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
The problem of detecting whether a test sample is from in-distribution (...
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Confident Multiple Choice Learning
Ensemble methods are arguably the most trustworthy techniques for boosti...
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