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Multi-grained Attention with Object-level Grounding for Visual Question Answering
Attention mechanisms are widely used in Visual Question Answering (VQA) to search for visual clues related to the question.
M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search
In order to effectively train the agent from sparse rewards, we combine MCTS with the neural policy to generate trajectories yielding more positive rewards.
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