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Graph Representations for Higher-Order Logic and Theorem Proving, 2019
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Isabelle: A generic theorem prover , volume 828
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Bleu: a method for automatic evaluation of machine translation
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Learning to Prove Theorems by Learning to Generate Theorems, 2020
Mingzhe Wang and Jia Deng · 2002
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Chin-Yew Lin · 2004
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Language models are few-shot learners
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Mizar in a nutshell
Adam Grabowski, Artur Kornilowicz, and Adam Naumowicz · 2010
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Extending Sledgehammer with SMT solvers
Jasmin Christian Blanchette, Sascha Böhme, and Lawrence C Paulson · 2011
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Wojciech Zaremba and Ilya Sutskever · 2014
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Neural machine translation by jointly learning to align and translate
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DeepMath - Deep Sequence Models for Premise Selection
Alexander A. Alemi, François Chollet, Niklas Eén, Geoffrey Irving, Christian Szegedy, and Josef Urban · 2016
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Initial experiments with statistical conjecturing over large formal corpora
Thibault Gauthier, Cezary Kaliszyk, and Josef Urban · 2016
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Neural gpus learn algorithms
Lukasz Kaiser and Ilya Sutskever · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean · 2016
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Hierarchical attention networks for document classification
Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alexander J. Smola, and Eduard H. Hovy · 2016
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Thibault Gauthier, Cezary Kaliszyk, and Josef Urban · 2017
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Convolutional sequence to sequence learning
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin · 2017
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HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving
Cezary Kaliszyk, François Chollet, and Christian Szegedy · 2017
Analysing mathematical reasoning abilities of neural models
David Saxton, Edward Grefenstette, Felix Hill, and Pushmeet Kohli · 2019
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Learning to Prove Theorems via Interacting with Proof Assistants
Kaiyu Yang and Jia Deng · 2019
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HIBERT: document level pre-training of hierarchical bidirectional transformers for document summarization
Xingxing Zhang, Furu Wei, and Ming Zhou · 2019
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Learning to advise an equational prover
Chad E Brown, Bartosz Piotrowski, and Josef Urban · 2020
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François Charton, Amaury Hayat, and Guillaume Lample · 2020
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Program induction by rationale generation: Learning to solve and explain algebraic word problems
Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Can neural networks understand logical entailment?
Richard Evans, David Saxton, David Amos, Pushmeet Kohli, and Edward Grefenstette · 2018
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Pamper: Proof method recommendation system for isabelle/hol
Yutaka Nagashima and Yilun He · 2018
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Goal-oriented conjecturing for isabelle/hol, 2018
Yutaka Nagashima and Julian Parsert · 2018
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Neural arithmetic logic units
Andrew Trask, Felix Hill, Scott E. Reed, Jack W. Rae, Chris Dyer, and Phil Blunsom · 2018
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HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving
Kshitij Bansal, Sarah M. Loos, Markus N. Rabe, Christian Szegedy, and Stewart Wilcox · 2019
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Bernd Finkbeiner, Christopher Hahn, Markus N Rabe, and Frederik Schmitt · 2020
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Simple dataset for proof method recommendation in isabelle/hol
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Markus N Rabe, Dennis Lee, Kshitij Bansal, and Christian Szegedy · 2020
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