Built on

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: RoBERTa: A robustly optimized BERT pretraining approach (2019). https://doi.org/10.48550/ARXIV.1907.11692, https://arxiv.org/abs/1907.11692
Original
1907
Earlier work this paper cites.
Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using Siamese BERT-Networks. CoRR abs/1908.10084
Original
1908
Earlier work this paper cites.
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J.: Exploring the limits of transfer learning with a unified text-to-text transformer. CoRR abs/1910.10683
Original
1910
Earlier work this paper cites.
Luhn, H.P.: A statistical approach to mechanized encoding and searching of literary information. IBM Journal of Research and Development 1
1957
Earlier work this paper cites.
Jones, K.S.: A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation 28
1972
Earlier work this paper cites.
Owre, S., Rushby, J.M., Shankar, N.: PVS: A prototype verification system. In: International Conference on Automated Deduction. pp. 748–752. Springer (1992)
1992
Earlier work this paper cites.
Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., Shah, R.: Signature verification using a "siamese" time delay neural network. Advances in neural information processing systems 6
1993
Earlier work this paper cites.
Efron, B., Tibshirani, R.J.: An Introduction to the Bootstrap. No. 57 in Monographs on Statistics and Applied Probability, Chapman & Hall/CRC, Boca Raton, Florida, USA (1993)
1993
Earlier work this paper cites.
Gage, P.: A new algorithm for data compression. C Users J. 12
1994
Earlier work this paper cites.
Schulz, S.: E – A Brainiac Theorem Prover. Journal of AI Communications 15
2002
Earlier work this paper cites.
Hsu, C.W., Chang, C.C., Lin, C.J.: A practical guide to support vector classification. Tech. rep., Department of Computer Science, National Taiwan University (2003), http://www.csie.ntu.edu.tw/~cjlin/papers.html
2003
Earlier work this paper cites.
Otten, J., Bibel, W.: leancop: lean connection-based theorem proving. Journal of Symbolic Computation 36
2003
Earlier work this paper cites.
Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: Language models are few-shot learners. CoRR abs/2005.14165
Original
2005
Earlier work this paper cites.
Li, S., Zhao, Y., Varma, R., Salpekar, O., Noordhuis, P., Li, T., Paszke, A., Smith, J., Vaughan, B., Damania, P., Chintala, S.: PyTorch Distributed: Experiences on accelerating data parallel training. CoRR abs/2006.15704
Original
2006
Earlier work this paper cites.
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge, UK (2008), http://nlp.stanford.edu/IR-book/information-retrieval-book.html
2008
Earlier work this paper cites.
Biere, A., Heule, M., van Maaren, H., Walsh, T. (eds.): Handbook of Satisfiability. IOS Press (2009)
2009
Earlier work this paper cites.

Similar

Urban, J., Vyskočil, J., Štěpánek, P.: Malecop machine learning connection prover. In: International Conference on Automated Reasoning with Analytic Tableaux and Related Methods. pp. 263–277. Springer (2011)
2011
Cited alongside, same era.
Komendantskaya, E., Heras, J., Grov, G.: Machine learning in proof general: Interfacing interfaces. arXiv preprint arXiv:1212.3618 (2012)
Original
2012
Cited alongside, same era.
Huang, P.S., He, X., Gao, J., Deng, L., Acero, A., Heck, L.: Learning deep structured semantic models for web search using clickthrough data. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management. p. 2333–2338. CIKM ’13, Association for Computing Machinery, New York, NY, USA (2013). https://doi.org/10.1145/2505515.2505665, https://doi.org/10.1145/2505515.2505665
2013
Cited alongside, same era.
Kühlwein, D., Blanchette, J.C., Kaliszyk, C., Urban, J.: Mash: machine learning for sledgehammer. In: International Conference on Interactive Theorem Proving. pp. 35–50. Springer (2013)

Then

Huang, D., Dhariwal, P., Song, D., Sutskever, I.: Gamepad: A learning environment for theorem proving. arXiv preprint arXiv:1806.00608 (2018)
Original
2018
Later among the works it cites.
Mitra, B., Craswell, N.: (2018)
2018
Later among the works it cites.
Bansal, K., Loos, S., Rabe, M., Szegedy, C., Wilcox, S.: Holist: An environment for machine learning of higher order logic theorem proving. In: International Conference on Machine Learning. pp. 454–463. PMLR (2019)
2019
Later among the works it cites.
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (Jun 2019). https://doi.org/10.18653/v1/N19-1423, https://aclanthology.org/N19-1423

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alphaXiv searches the wider corpus for related work and actual follow-ups.

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Reading the bibliography…

2023

CoProver: A Recommender System for Proof Construction

Yeh, Eric, Hitaj, Briland, Owre, Sam et al.

Understand

Interactive Theorem Provers (ITPs) are an indispensable tool in the arsenal of formal method experts as a platform for construction and (formal) verification of proofs.

The complexity of the proofs in conjunction with the level of expertise typically required for the process to succeed can often hinder the adoption of ITPs.
A recent strain of work has investigated methods to incorporate machine learning models trained on ITP user activity traces as a viable path towards full automation.
While a valuable line of investigation, many problems still require human supervision to be completed fully, thus applying learning methods to assist the user with useful recommendations can prove more fruitful.

Built on

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: RoBERTa: A robustly optimized BERT pretraining approach (2019). https://doi.org/10.48550/ARXIV.1907.11692, https://arxiv.org/abs/1907.11692
Original
1907
Earlier work this paper cites.
Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using Siamese BERT-Networks. CoRR abs/1908.10084
Original
1908
Earlier work this paper cites.
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J.: Exploring the limits of transfer learning with a unified text-to-text transformer. CoRR abs/1910.10683
Original
1910
Earlier work this paper cites.
Luhn, H.P.: A statistical approach to mechanized encoding and searching of literary information. IBM Journal of Research and Development 1
1957
Earlier work this paper cites.
Jones, K.S.: A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation 28
1972
Earlier work this paper cites.
Owre, S., Rushby, J.M., Shankar, N.: PVS: A prototype verification system. In: International Conference on Automated Deduction. pp. 748–752. Springer (1992)
1992
Earlier work this paper cites.
Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., Shah, R.: Signature verification using a "siamese" time delay neural network. Advances in neural information processing systems 6
1993
Earlier work this paper cites.
Efron, B., Tibshirani, R.J.: An Introduction to the Bootstrap. No. 57 in Monographs on Statistics and Applied Probability, Chapman & Hall/CRC, Boca Raton, Florida, USA (1993)
1993
Earlier work this paper cites.
Gage, P.: A new algorithm for data compression. C Users J. 12
1994
Earlier work this paper cites.
Schulz, S.: E – A Brainiac Theorem Prover. Journal of AI Communications 15
2002
Earlier work this paper cites.
Hsu, C.W., Chang, C.C., Lin, C.J.: A practical guide to support vector classification. Tech. rep., Department of Computer Science, National Taiwan University (2003), http://www.csie.ntu.edu.tw/~cjlin/papers.html
2003
Earlier work this paper cites.
Otten, J., Bibel, W.: leancop: lean connection-based theorem proving. Journal of Symbolic Computation 36
2003
Earlier work this paper cites.
Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: Language models are few-shot learners. CoRR abs/2005.14165
Original
2005
Earlier work this paper cites.
Li, S., Zhao, Y., Varma, R., Salpekar, O., Noordhuis, P., Li, T., Paszke, A., Smith, J., Vaughan, B., Damania, P., Chintala, S.: PyTorch Distributed: Experiences on accelerating data parallel training. CoRR abs/2006.15704
Original
2006
Earlier work this paper cites.
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge, UK (2008), http://nlp.stanford.edu/IR-book/information-retrieval-book.html
2008
Earlier work this paper cites.
Biere, A., Heule, M., van Maaren, H., Walsh, T. (eds.): Handbook of Satisfiability. IOS Press (2009)
2009
Earlier work this paper cites.

Similar

Urban, J., Vyskočil, J., Štěpánek, P.: Malecop machine learning connection prover. In: International Conference on Automated Reasoning with Analytic Tableaux and Related Methods. pp. 263–277. Springer (2011)
2011
Cited alongside, same era.
Komendantskaya, E., Heras, J., Grov, G.: Machine learning in proof general: Interfacing interfaces. arXiv preprint arXiv:1212.3618 (2012)
Original
2012
Cited alongside, same era.
Huang, P.S., He, X., Gao, J., Deng, L., Acero, A., Heck, L.: Learning deep structured semantic models for web search using clickthrough data. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management. p. 2333–2338. CIKM ’13, Association for Computing Machinery, New York, NY, USA (2013). https://doi.org/10.1145/2505515.2505665, https://doi.org/10.1145/2505515.2505665
2013
Cited alongside, same era.
Kühlwein, D., Blanchette, J.C., Kaliszyk, C., Urban, J.: Mash: machine learning for sledgehammer. In: International Conference on Interactive Theorem Proving. pp. 35–50. Springer (2013)

Then

Huang, D., Dhariwal, P., Song, D., Sutskever, I.: Gamepad: A learning environment for theorem proving. arXiv preprint arXiv:1806.00608 (2018)
Original
2018
Later among the works it cites.
Mitra, B., Craswell, N.: (2018)
2018
Later among the works it cites.
Bansal, K., Loos, S., Rabe, M., Szegedy, C., Wilcox, S.: Holist: An environment for machine learning of higher order logic theorem proving. In: International Conference on Machine Learning. pp. 454–463. PMLR (2019)
2019
Later among the works it cites.
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (Jun 2019). https://doi.org/10.18653/v1/N19-1423, https://aclanthology.org/N19-1423

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

2013

Cited alongside, same era.

Gransden, T., Walkinshaw, N., Raman, R.: Sepia: search for proofs using inferred automata. In: International Conference on Automated Deduction. pp. 246–255. Springer (2015)

2015

Cited alongside, same era.

Kaliszyk, C., Urban, J.: Femalecop: Fairly efficient machine learning connection prover. In: Logic for Programming, Artificial Intelligence, and Reasoning. pp. 88–96. Springer (2015)

2015

Cited alongside, same era.

Blanchette, J.C., Greenaway, D., Kaliszyk, C., Kühlwein, D., Urban, J.: A learning-based fact selector for isabelle/hol. Journal of Automated Reasoning 57

2016

Cited alongside, same era.

Irving, G., Szegedy, C., Alemi, A.A., Eén, N., Chollet, F., Urban, J.: Deepmath-deep sequence models for premise selection. Advances in Neural Information Processing Systems 29

2016

Cited alongside, same era.

Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 1715–1725. Association for Computational Linguistics, Berlin, Germany (Aug 2016). https://doi.org/10.18653/v1/P16-1162, https://aclanthology.org/P16-1162

2016

Cited alongside, same era.

Whalen, D.: Holophrasm: a neural automated theorem prover for higher-order logic. arXiv preprint arXiv:1608.02644 (2016)

Original

2016

Cited alongside, same era.

Jakubův, J., Urban, J.: Enigma: efficient learning-based inference guiding machine. In: Intelligent Computer Mathematics: 10th International Conference, CICM 2017, Edinburgh, UK, July 17-21, 2017, Proceedings 10. pp. 292–302. Springer (2017)

2017

Cited alongside, same era.

Loos, S., Irving, G., Szegedy, C., Kaliszyk, C.: Deep network guided proof search. arXiv preprint arXiv:1701.06972 (2017)

Original

2017

Cited alongside, same era.

2019