Browse > Miscellaneous > Automated Theorem Proving
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# Automated Theorem Proving Edit

16 papers with code · Miscellaneous

TREND DATASET BEST METHOD PAPER TITLE PAPER CODE COMPARE

# HOList: An Environment for Machine Learning of Higher-Order Theorem Proving

5 Apr 2019tensorflow/deepmath

We present an environment, benchmark, and deep learning driven automated theorem prover for higher-order logic.

700

# HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving

1 Mar 2017tensorflow/deepmath

We propose various machine learning tasks that can be performed on this dataset, and discuss their significance for theorem proving.

700

# jsCoq: Towards Hybrid Theorem Proving Interfaces

25 Jan 2017ejgallego/jscoq

We describe jsCcoq, a new platform and user environment for the Coq interactive proof assistant.

329

# Learning to Prove Theorems via Interacting with Proof Assistants

21 May 2019princeton-vl/CoqGym

Proof assistants offer a formalism that resembles human mathematical reasoning, representing theorems in higher-order logic and proofs as high-level tactics.

140

# On-demand Injection of Lexical Knowledge for Recognising Textual Entailment

We approach the recognition of textual entailment using logical semantic representations and a theorem prover.

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# GamePad: A Learning Environment for Theorem Proving

In this paper, we introduce a system called GamePad that can be used to explore the application of machine learning methods to theorem proving in the Coq proof assistant.

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# GamePad: A Learning Environment for Theorem Proving

In this paper, we introduce a system called GamePad that can be used to explore the application of machine learning methods to theorem proving in the Coq proof assistant.

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# Holophrasm: a neural Automated Theorem Prover for higher-order logic

8 Aug 2016dwhalen/holophrasm

I propose a system for Automated Theorem Proving in higher order logic using deep learning and eschewing hand-constructed features.

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# DeepMath - Deep Sequence Models for Premise Selection

We study the effectiveness of neural sequence models for premise selection in automated theorem proving, one of the main bottlenecks in the formalization of mathematics.

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# Generating Correctness Proofs with Neural Networks

Foundational verification allows programmers to build software which has been empirically shown to have high levels of assurance in a variety of important domains.

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