Generating correctness proofs with neural networks
暂无分享,去创建一个
Sorin Lerner | Lawrence K. Saul | Yousef Alhessi | Alex Sanchez-Stern | L. Saul | Sorin Lerner | Alex Sanchez-Stern | Yousef Alhessi
[1] Lawrence C. Paulson,et al. Natural Deduction as Higher-Order Resolution , 1986, J. Log. Program..
[2] Dawn Xiaodong Song,et al. GamePad: A Learning Environment for Theorem Proving , 2018, ICLR.
[3] Sarah M. Loos,et al. HOList: An Environment for Machine Learning of Higher-Order Theorem Proving (extended version) , 2019, ArXiv.
[4] Hugo Herbelin,et al. The Coq proof assistant : reference manual, version 6.1 , 1997 .
[5] Cezary Kaliszyk,et al. Deep Network Guided Proof Search , 2017, LPAR.
[6] Xavier Leroy,et al. CompCert: Practical Experience on Integrating and Qualifying a Formally Verified Optimizing Compiler , 2018 .
[7] Premkumar T. Devanbu,et al. A Survey of Machine Learning for Big Code and Naturalness , 2017, ACM Comput. Surv..
[8] Jia Deng,et al. Learning to Prove Theorems via Interacting with Proof Assistants , 2019, ICML.
[9] Xuejun Yang,et al. Finding and understanding bugs in C compilers , 2011, PLDI '11.
[10] Adam Chlipala,et al. Certified Programming with Dependent Types - A Pragmatic Introduction to the Coq Proof Assistant , 2013 .
[11] Ekaterina Komendantskaya,et al. Neural Networks for Proof-Pattern Recognition , 2012, ICANN.
[12] David Walker,et al. Example-directed synthesis: a type-theoretic interpretation , 2016, POPL.
[13] Xi Wang,et al. Verdi: a framework for implementing and formally verifying distributed systems , 2015, PLDI.
[14] Martin T. Vechev,et al. PHOG: Probabilistic Model for Code , 2016, ICML.
[15] Tao Wang,et al. Convolutional Neural Networks over Tree Structures for Programming Language Processing , 2014, AAAI.
[16] Peter-Michael Osera,et al. Type-and-example-directed program synthesis , 2015, PLDI.
[17] Andrei Voronkov,et al. First-Order Theorem Proving and Vampire , 2013, CAV.
[18] Michael Norrish,et al. seL4: formal verification of an OS kernel , 2009, SOSP '09.
[19] J. Gregory Morrisett,et al. Toward a verified relational database management system , 2010, POPL '10.
[20] Akifumi Imanishi,et al. Towards Proof Synthesis Guided by Neural Machine Translation for Intuitionistic Propositional Logic , 2017, ArXiv.
[21] Tao Wang,et al. TBCNN: A Tree-Based Convolutional Neural Network for Programming Language Processing , 2014, ArXiv.
[22] Jónathan Heras,et al. ACL2(ml): Machine-Learning for ACL2 , 2014, ACL2.
[23] Cezary Kaliszyk,et al. HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving , 2017, ICLR.
[24] Thibault Gauthier,et al. TacticToe: Learning to Reason with HOL4 Tactics , 2017, LPAR.
[25] Thibault Gauthier,et al. Learning to Reason with HOL4 tactics , 2017, ICLP 2017.
[26] Adam Chlipala,et al. Verifying a high-performance crash-safe file system using a tree specification , 2017, SOSP.
[27] Dan Roth,et al. Learning invariants using decision trees and implication counterexamples , 2016, POPL.
[28] Xavier Leroy,et al. Formal verification of a realistic compiler , 2009, CACM.
[29] Anna Philippou,et al. Tools and Algorithms for the Construction and Analysis of Systems , 2018, Lecture Notes in Computer Science.
[30] Sumit Gulwani,et al. Dimensions in program synthesis , 2010, Formal Methods in Computer Aided Design.
[31] Gudmund Grov,et al. Machine Learning in Proof General: Interfacing Interfaces , 2012, UITP.
[32] Cezary Kaliszyk,et al. Hammer for Coq: Automation for Dependent Type Theory , 2018, Journal of Automated Reasoning.
[33] ZdancewicSteve,et al. Type-and-example-directed program synthesis , 2015 .
[34] Ole Tange,et al. GNU Parallel: The Command-Line Power Tool , 2011, login Usenix Mag..
[35] Andrew W. Appel,et al. Verification of a Cryptographic Primitive: SHA-256 , 2015, TOPL.
[36] Stephan Schulz,et al. System Description: E 1.8 , 2013, LPAR.
[37] Nikolaj Bjørner,et al. Z3: An Efficient SMT Solver , 2008, TACAS.
[38] Mislav Balunovic,et al. Learning to Solve SMT Formulas , 2018, NeurIPS.
[39] Truyen Tran,et al. A deep language model for software code , 2016, FSE 2016.
[40] Fan Long,et al. Automatic inference of code transforms for patch generation , 2017, ESEC/SIGSOFT FSE.
[41] Yoshua Bengio,et al. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.
[42] Josef Urban,et al. DeepMath - Deep Sequence Models for Premise Selection , 2016, NIPS.
[43] Ruzica Piskac,et al. Complete completion using types and weights , 2013, PLDI.
[44] Sarah M. Loos,et al. Graph Representations for Higher-Order Logic and Theorem Proving , 2019, AAAI.