Search Results for author: Timothée Lacroix

Found 7 papers, 5 papers with code

Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs

2 code implementations21 Oct 2022 Albert Q. Jiang, Sean Welleck, Jin Peng Zhou, Wenda Li, Jiacheng Liu, Mateja Jamnik, Timothée Lacroix, Yuhuai Wu, Guillaume Lample

In this work, we introduce Draft, Sketch, and Prove (DSP), a method that maps informal proofs to formal proof sketches, and uses the sketches to guide an automated prover by directing its search to easier sub-problems.

Ranked #2 on Automated Theorem Proving on miniF2F-test (Pass@100 metric)

Automated Theorem Proving Language Modelling

HyperTree Proof Search for Neural Theorem Proving

no code implementations23 May 2022 Guillaume Lample, Marie-Anne Lachaux, Thibaut Lavril, Xavier Martinet, Amaury Hayat, Gabriel Ebner, Aurélien Rodriguez, Timothée Lacroix

With a similar computational budget, we improve the state of the art on the Lean-based miniF2F-curriculum dataset from 31% to 42% proving accuracy.

Automated Theorem Proving

Tensor Decompositions for temporal knowledge base completion

2 code implementations ICLR 2020 Timothée Lacroix, Guillaume Obozinski, Nicolas Usunier

Additionally, we propose a new dataset for knowledge base completion constructed from Wikidata, larger than previous benchmarks by an order of magnitude, as a new reference for evaluating temporal and non-temporal link prediction methods.

Knowledge Base Completion Link Prediction +2

Projected Canonical Decomposition for Knowledge Base Completion

no code implementations25 Sep 2019 Timothée Lacroix, Guillaume Obozinski, Joan Bruna, Nicolas Usunier

However, as we show in this paper through experiments on standard benchmarks of link prediction in knowledge bases, ComplEx, a variant of CP, achieves similar performances to recent approaches based on Tucker decomposition on all operating points in terms of number of parameters.

Knowledge Base Completion Link Prediction

TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games

2 code implementations1 Nov 2016 Gabriel Synnaeve, Nantas Nardelli, Alex Auvolat, Soumith Chintala, Timothée Lacroix, Zeming Lin, Florian Richoux, Nicolas Usunier

We present TorchCraft, a library that enables deep learning research on Real-Time Strategy (RTS) games such as StarCraft: Brood War, by making it easier to control these games from a machine learning framework, here Torch.

BIG-bench Machine Learning Starcraft

Cannot find the paper you are looking for? You can Submit a new open access paper.