24.4k citations · 66.7k across the 24 of their papers we have counts for
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Definition Grounding in Theory-Level Auto-formalization
Leyan Pan, Christian Szegedy, Frederick Rajasekaran +3
LLM-based auto-formalization systems can now generate formally verified Lean code at unprecedented rates. However, a formalization advances mathematical research only when further…
Don't Trust: Verify -- Grounding LLM Quantitative Reasoning with Autoformalization
Jin Peng Zhou, Charles Staats, Wenda Li +3
Large language models (LLM), such as Google's Minerva and OpenAI's GPT families, are becoming increasingly capable of solving mathematical quantitative reasoning problems. However,…
HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving
Cezary Kaliszyk, François Chollet, Christian Szegedy
Large computer-understandable proofs consist of millions of intermediate logical steps. The vast majority of such steps originate from manually selected and manually guided heurist…
Deep Network Guided Proof Search
Sarah Loos, Geoffrey Irving, Christian Szegedy +1
Deep learning techniques lie at the heart of several significant AI advances in recent years including object recognition and detection, image captioning, machine translation, spee…
DeepMath - Deep Sequence Models for Premise Selection
Alex A. Alemi, Francois Chollet, Niklas Een +3
We study the effectiveness of neural sequence models for premise selection in automated theorem proving, one of the main bottlenecks in the formalization of mathematics. We propose…