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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 280Source ↗PDF ↗Scholar ↗
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 600Source ↗Scholar ↗
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.…