Automated Theorem Proving
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Greatest papers with code
HOList: An Environment for Machine Learning of Higher-Order Theorem Proving
We present an environment, benchmark, and deep learning driven automated theorem prover for higher-order logic.
#2 best model for
Automated Theorem Proving
on HOList benchmark
HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving
We propose various machine learning tasks that can be performed on this dataset, and discuss their significance for theorem proving.
#3 best model for
Automated Theorem Proving
on HolStep (Unconditional)
jsCoq: Towards Hybrid Theorem Proving Interfaces
We describe jsCcoq, a new platform and user environment for the Coq interactive proof assistant.
Learning to Prove Theorems via Interacting with Proof Assistants
Proof assistants offer a formalism that resembles human mathematical reasoning, representing theorems in higher-order logic and proofs as high-level tactics.
SOTA for Automated Theorem Proving on CoqGym
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.
AUTOMATED THEOREM PROVING INFORMATION RETRIEVAL NATURAL LANGUAGE INFERENCE QUESTION ANSWERING
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.
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.
Holophrasm: a neural Automated Theorem Prover for higher-order logic
I propose a system for Automated Theorem Proving in higher order logic using deep learning and eschewing hand-constructed features.
#3 best model for
Automated Theorem Proving
on Metamath set.mm
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.
Generating Correctness Proofs with Neural Networks
Under submission to POPL 2020 2019
• UCSD-PL/proverbot9001
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Foundational verification allows programmers to build software which has been empirically shown to have high levels of assurance in a variety of important domains.