Scalable Differentiable Proving
Tim discusses the challenges of transforming traditional algorithms into differentiable computation graphs, particularly the importance of batch processing for scalability. He highlights the inefficiencies of training with noisy proofs when starting from random initializations and introduces a clever solution: using a neural link prediction model as a regularizer to enhance learning of symbol similarities, which ultimately aids the differentiable theorem prover in making more effective multi-hop inferences.In this clip
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NLP Highlights
19 - End-to-end Differentiable Proving, with Tim Rocktäschel
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