Search Results for author: Bartosz Piotrowski

Found 10 papers, 4 papers with code

Machine-Learned Premise Selection for Lean

1 code implementation17 Mar 2023 Bartosz Piotrowski, Ramon Fernández Mir, Edward Ayers

For this purpose, we designed a custom version of the random forest model, trained in an online fashion.

MizAR 60 for Mizar 50

no code implementations12 Mar 2023 Jan Jakubův, Karel Chvalovský, Zarathustra Goertzel, Cezary Kaliszyk, Mirek Olšák, Bartosz Piotrowski, Stephan Schulz, Martin Suda, Josef Urban

As a present to Mizar on its 50th anniversary, we develop an AI/TP system that automatically proves about 60\% of the Mizar theorems in the hammer setting.

Magnushammer: A Transformer-Based Approach to Premise Selection

no code implementations8 Mar 2023 Maciej Mikuła, Szymon Tworkowski, Szymon Antoniak, Bartosz Piotrowski, Albert Qiaochu Jiang, Jin Peng Zhou, Christian Szegedy, Łukasz Kuciński, Piotr Miłoś, Yuhuai Wu

By combining \method with a language-model-based automated theorem prover, we further improve the state-of-the-art proof success rate from $57. 0\%$ to $71. 0\%$ on the PISA benchmark using $4$x fewer parameters.

Automated Theorem Proving Language Modelling +1

Online Machine Learning Techniques for Coq: A Comparison

no code implementations12 Apr 2021 Liao Zhang, Lasse Blaauwbroek, Bartosz Piotrowski, Prokop Černý, Cezary Kaliszyk, Josef Urban

Learning happens in an online manner, meaning that Tactician's machine learning model is updated immediately every time the user performs a step in an interactive proof.

BIG-bench Machine Learning

Stateful Premise Selection by Recurrent Neural Networks

no code implementations11 Mar 2020 Bartosz Piotrowski, Josef Urban

In this work, we develop a new learning-based method for selecting facts (premises) when proving new goals over large formal libraries.

Data Augmentation Translation

ENIGMA Anonymous: Symbol-Independent Inference Guiding Machine (system description)

no code implementations13 Feb 2020 Jan Jakubův, Karel Chvalovský, Miroslav Olšák, Bartosz Piotrowski, Martin Suda, Josef Urban

For the neural guidance, we use symbol-independent graph neural networks (GNNs) and their embedding of the terms and clauses.

Can Neural Networks Learn Symbolic Rewriting?

no code implementations7 Nov 2019 Bartosz Piotrowski, Josef Urban, Chad E. Brown, Cezary Kaliszyk

This work investigates if the current neural architectures are adequate for learning symbolic rewriting.

Machine Translation Translation

Guiding Inferences in Connection Tableau by Recurrent Neural Networks

1 code implementation20 May 2019 Bartosz Piotrowski, Josef Urban

We present a dataset and experiments on applying recurrent neural networks (RNNs) for guiding clause selection in the connection tableau proof calculus.

Automated Theorem Proving Machine Translation

ATPboost: Learning Premise Selection in Binary Setting with ATP Feedback

1 code implementation9 Feb 2018 Bartosz Piotrowski, Josef Urban

ATPboost is a system for solving sets of large-theory problems by interleaving ATP runs with state-of-the-art machine learning of premise selection from the proofs.

Binary Classification General Classification

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