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We consider the task of automated theorem proving, a key AI task.
Learning to reason in large theories without imitation
Bansal, K., Loos, S. M., Rabe, M. N., and Szegedy, C · 1905
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J · 1992
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Formal verification in hardware design: a survey
Kern, C. and Greenstreet, M. R · 1999
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E–a brainiac theorem prover
Schulz, S · 2002
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Formal proof sketches
Wiedijk, F · 2003
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Coq’art: the calculus of inductive constructions, 2004
Bertot, Y. and Castéran, P · 2004
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Mptp–motivation, implementation, first experiments
Urban, J · 2004
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A theorem proving approach to analysis of secure information flow
Darvas, Á., Hähnle, R., and Sands, D · 2005
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Bandit based monte-carlo planning
Kocsis, L. and Szepesvári, C · 2006
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A brief overview of hol4
Slind, K. and Norrish, M · 2008
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Malarea sg1-machine learner for automated reasoning with semantic guidance
Urban, J., Sutcliffe, G., Pudlák, P., and Vyskočil, J · 2008
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HOL Light: An overview
Harrison, J · 2009
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Automated theory formation in pure mathematics
Colton, S · 2012
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Machine learner for automated reasoning 0.4 and 0.5
Kaliszyk, C., Urban, J., and Vyskočil, J · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Learning-assisted theorem proving with millions of lemmas
Kaliszyk, C. and Urban, J · 2015
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Lemmatization for stronger reasoning in large theories
Kaliszyk, C., Urban, J., and Vyskočil, J · 2015
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Certikos: An extensible architecture for building certified concurrent { \{ OS } \} kernels
Gu, R., Shao, Z., Chen, H., Wu, X. N., Kim, J., Sjöberg, V., and Costanzo, D · 2016
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Deepmath-deep sequence models for premise selection
Irving, G., Szegedy, C., Alemi, A. A., Eén, N., Chollet, F., and Urban, J · 2016
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Holophrasm: a neural automated theorem prover for higher-order logic
Reinforcement learning of theorem proving
Kaliszyk, C., Urban, J., Michalewski, H., and Olšák, M · 2018
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Atpboost: Learning premise selection in binary setting with atp feedback
Piotrowski, B. and Urban, J · 2018
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Learning goal embeddings via self-play for hierarchical reinforcement learning
Sukhbaatar, S., Denton, E., Szlam, A., and Fergus, R · 2018
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First experiments with data driven conjecturing
Chvalovskỳ, K., Gauthier, T., and Urban, J · 2019
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Learning dynamic polynomial proofs
Fawzi, A., Malinowski, M., Fawzi, H., and Fawzi, O · 2019
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Whalen, D · 2016
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Zombori, Z., Csiszárik, A., Michalewski, H., Kaliszyk, C., and Urban, J · 2016
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Deep network guided proof search
Loos, S., Irving, G., Szegedy, C., and Kaliszyk, C · 2017
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Intrinsic motivation and automatic curricula via asymmetric self-play
Sukhbaatar, S., Lin, Z., Kostrikov, I., Synnaeve, G., Szlam, A., and Fergus, R · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Learning to solve smt formulas
Balunovic, M., Bielik, P., and Vechev, M · 2018
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Adversarial goal generation for intrinsic motivation
Durugkar, I. and Stone, P · 2018
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Huang, D · 2019
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Hammering mizar by learning clause guidance
Jakubův, J. and Urban, J · 2019
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Mathematical reasoning in latent space
Lee, D., Szegedy, C., Rabe, M. N., Loos, S. M., and Bansal, K · 2019
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Metamath: A Computer Language for Mathematical Proofs
Megill, N. and Wheeler, D · 2019
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Graph representations for higher-order logic and theorem proving
Paliwal, A., Loos, S., Rabe, M., Bansal, K., and Szegedy, C · 2019
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Formalizing 100 theorems
Wiedijk, F · 2019
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Learning to prove theorems via interacting with proof assistants
Yang, K. and Deng, J · 2019
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Generative language modeling for automated theorem proving
Polu, S. and Sutskever, I · 2020
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First neural conjecturing datasets and experiments
Urban, J. and Jakubův, J · 2020
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