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Solving real-life sequential decision making problems under partial observability involves an exploration-exploitation problem.
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Learning to diagnose: Assimilating clinical narratives using deep reinforcement learning
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Semantic image inpainting with deep generative models
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Handling incomplete heterogeneous data using vaes
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REFUEL: exploring sparse features in deep reinforcement learning for fast disease diagnosis
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Joint active feature acquisition and classification with variable-size set encoding
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Action-driven visual object tracking with deep reinforcement learning
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Advances in variational inference
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INVASE: instance-wise variable selection using neural networks
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Odin: Optimal discovery of high-value information using model-based deep reinforcement learning
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VAEM: a deep generative model for heterogeneous mixed type data
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Maximizing information gain in partially observable environments via prediction rewards
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