Neural Proof Construction
Tim discusses the innovative approach of using a neural network to construct proofs, attaching a differentiable success score to each proof. He explains the limitations of their symbolic unification variant, particularly the exclusion of function terms, which contrasts with traditional systems like Prolog. A point of confusion arises regarding the unification of specific proof trees, highlighting the complexities involved in their proof construction methodology.In this clip
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NLP Highlights
19 - End-to-end Differentiable Proving, with Tim Rocktäschel
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