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Marie-anne Lachaux

4 accepted papers

2022

HyperTree Proof Search for Neural Theorem Proving

NeurIPS 2022accept

We propose an online training procedure for a transformer-based automated theorem prover. Our approach leverages a new search algorithm, HyperTree Proof Search (HTPS), that learns from previous proof searches through online training, allowing it to generalize to domains far from the training distrib…

Cited by 158SourcePDFScholar
2021

DOBF: A Deobfuscation Pre-Training Objective for Programming Languages

NeurIPS 2021poster

Recent advances in self-supervised learning have dramatically improved the state of the art on a wide variety of tasks. However, research in language model pre-training has mostly focused on natural languages, and it is unclear whether models like BERT and its variants provide the best pre-training…

2020

Poly-encoders: Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring

ICLR 2020poster

The use of deep pre-trained transformers has led to remarkable progress in a number of applications (Devlin et al., 2018). For tasks that make pairwise comparisons between sequences, matching a given input with a corresponding label, two approaches are common: Cross-encoders performing full self-att…

Cited by 600SourceScholar
2020

Unsupervised Translation of Programming Languages

NeurIPS 2020poster

A transcompiler, also known as source-to-source translator, is a system that converts source code from a high-level programming language (such as C++ or Python) to another. Transcompilers are primarily used for interoperability, and to port codebases written in an obsolete or deprecated language (e.…