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Conformal retrofitting via Riemannian manifolds: distilling task-specific graphs into pretrained embeddings
We address these problems with two key contributions: (i) we propose a novel regularizer, a conformality regularizer, that preserves local geometry from the pretrained embeddings---enabling generalization to missing entities and (ii) a new Riemannian feedforward layer that learns to map pre-trained embeddings onto a non-Euclidean manifold that can better represent the entire graph.
Mimic and Rephrase: Reflective Listening in Open-Ended Dialogue
Reflective listening{--}demonstrating that you have heard your conversational partner{--}is key to effective communication.
Collaboration of AI Agents via Cooperative Multi-Agent Deep Reinforcement Learning
We train and evaluate our multi-agent methods against a team operating with a smart hand-coded policy.
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