Search Results for author: Miroslav Olšák

Found 10 papers, 4 papers with code

Graph2Tac: Learning Hierarchical Representations of Math Concepts in Theorem proving

no code implementations5 Jan 2024 Jason Rute, Miroslav Olšák, Lasse Blaauwbroek, Fidel Ivan Schaposnik Massolo, Jelle Piepenbrock, Vasily Pestun

G2T is an online model that is deeply integrated into the users' workflow and can adapt in real time to new Coq projects and their definitions.

Automated Theorem Proving Math

Alien Coding

no code implementations27 Jan 2023 Thibault Gauthier, Miroslav Olšák, Josef Urban

We introduce a self-learning algorithm for synthesizing programs for OEIS sequences.

Machine Translation Self-Learning +1

Machine Learning Meets The Herbrand Universe

1 code implementation7 Oct 2022 Jelle Piepenbrock, Josef Urban, Konstantin Korovin, Miroslav Olšák, Tom Heskes, Mikolaš Janota

In particular, we develop a GNN2RNN architecture based on an invariant graph neural network (GNN) that learns from problems and their solutions independently of symbol names (addressing the abundance of skolems), combined with a recurrent neural network (RNN) that proposes for each clause its instantiations.

The Isabelle ENIGMA

1 code implementation4 May 2022 Zarathustra A. Goertzel, Jan Jakubův, Cezary Kaliszyk, Miroslav Olšák, Jelle Piepenbrock, Josef Urban

We significantly improve the performance of the E automated theorem prover on the Isabelle Sledgehammer problems by combining learning and theorem proving in several ways.

Automated Theorem Proving

Learning Theorem Proving Components

1 code implementation21 Jul 2021 Karel Chvalovský, Jan Jakubův, Miroslav Olšák, Josef Urban

Saturation-style automated theorem provers (ATPs) based on the given clause procedure are today the strongest general reasoners for classical first-order logic.

Automated Theorem Proving

Fast and Slow Enigmas and Parental Guidance

1 code implementation14 Jul 2021 Zarathustra Goertzel, Karel Chvalovský, Jan Jakubův, Miroslav Olšák, Josef Urban

The second addition is motivated by fast weight-based rejection filters that are currently used in systems like E and Prover9.

The Role of Entropy in Guiding a Connection Prover

no code implementations31 May 2021 Zsolt Zombori, Josef Urban, Miroslav Olšák

This leads us to explore how the entropy of the inference selection implemented via the neural network influences the proof search.

Automated Theorem Proving Decision Making +1

ω-categorical structures avoiding height 1 identities

no code implementations13 Jun 2020 Manuel Bodirsky, Antoine Mottet, Miroslav Olšák, Jakub Opršal, Michael Pinsker, Ross Willard

The algebraic dichotomy conjecture for Constraint Satisfaction Problems (CSPs) of reducts of (infinite) finitely bounded homogeneous structures states that such CSPs are polynomial-time tractable if the model-complete core of the template has a pseudo-Siggers polymorphism, and NP-complete otherwise.

Logic Logic in Computer Science Rings and Algebras

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.

Property Invariant Embedding for Automated Reasoning

no code implementations27 Nov 2019 Miroslav Olšák, Cezary Kaliszyk, Josef Urban

This encoding represents symbols only by nodes in the graph, without giving the network any knowledge of the original labels.

Automated Theorem Proving

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