Fetching the paper…
Reading the bibliography…
Inspired by the recent evolution of deep neural networks (DNNs) in machine learning, we explore their application to PL-related topics.
Coquand, T., Huet, G.P.: The calculus of constructions. Inf. Comput. 76(2/3), 95–120 (1988)
1988
Earlier work this paper cites.
1989
Earlier work this paper cites.
Myers, G.: Approximately matching context-free languages. Inf. Process. Lett. 54(2), 85–92 (1995)
1995
Earlier work this paper cites.
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997)
1997
Earlier work this paper cites.
Sutton, R.S., Barto, A.G.: Introduction to Reinforcement Learning. MIT Press, Cambridge, MA, USA, 1st edn. (1998)
1998
Earlier work this paper cites.
Ishtiaq, S.S., O’Hearn, P.W.: BI as an assertion language for mutable data structures. In: Hankin, C., Schmidt, D. (eds.) Conference Record of POPL 2001: The 28th ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, London, UK, January 17-19, 2001. pp. 14–26. ACM (2001)
2001
Earlier work this paper cites.
Reynolds, J.C.: Separation logic: A logic for shared mutable data structures. In: 17th IEEE Symposium on Logic in Computer Science (LICS 2002), 22-25 July 2002, Copenhagen, Denmark, Proceedings. pp. 55–74. IEEE Computer Society (2002)
2002
Earlier work this paper cites.
Bengio, Y., Ducharme, R., Vincent, P., Janvin, C.: A neural probabilistic language model. Journal of Machine Learning Research 3, 1137–1155 (2003), http://www.jmlr.org/papers/v3/bengio03a.html
2003
Earlier work this paper cites.
The Coq development team
2004
Earlier work this paper cites.
Sørensen, M.H., Urzyczyn, P.: Lectures on the Curry-Howard Isomorphism, Volume 149 (Studies in Logic and the Foundations of Mathematics). Elsevier Science Inc., New York, NY, USA (2006)
2006
Earlier work this paper cites.
Norell, U.: Dependently typed programming in agda. In: Proceedings of TLDI’09: 2009 ACM SIGPLAN International Workshop on Types in Languages Design and Implementation, Savannah, GA, USA, January 24, 2009. pp. 1–2 (2009)
2009
Earlier work this paper cites.
Dahl, G.E., Yu, D., Deng, L., Acero, A.: Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Trans. Audio, Speech & Language Processing 20(1), 30–42 (2012)
2012
Cited alongside, same era.
Rondon, P.M., Bakst, A., Kawaguchi, M., Jhala, R.: Csolve: Verifying C with liquid types. In: Computer Aided Verification - 24th International Conference, CAV 2012, Berkeley, CA, USA, July 7-13, 2012 Proceedings. pp. 744–750 (2012)
2012
Cited alongside, same era.
2013
Cited alongside, same era.
2013
Cited alongside, same era.
Denton, E.L., Chintala, S., szlam, a., Fergus, R.: Deep generative image models using a laplacian pyramid of adversarial networks. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems 28, pp. 1486–1494. Curran Associates, Inc. (2015)
2015
Later among the works it cites.
Gregor, K., Danihelka, I., Graves, A., Rezende, D.J., Wierstra, D.: DRAW: A recurrent neural network for image generation. In: Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015. pp. 1462–1471 (2015)
2015
Later among the works it cites.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S.E., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7-12, 2015. pp. 1–9 (2015)
2015
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2014
Cited alongside, same era.
2014
Cited alongside, same era.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. CoRR abs/1412.6980 (2014)
2014
Cited alongside, same era.
2014
Cited alongside, same era.
2014
Cited alongside, same era.
Zaremba, W., Sutskever, I.: Learning to execute. CoRR abs/1410.4615 (2014)
2014
Cited alongside, same era.
Bancerek, G., Bylinski, C., Grabowski, A., Kornilowicz, A., Matuszewski, R., Naumowicz, A., Pak, K., Urban, J.: Mizar: State-of-the-art and beyond. In: Intelligent Computer Mathematics - International Conference, CICM 2015, Washington, DC, USA, July 13-17, 2015, Proceedings. pp. 261–279 (2015)
2015
Cited alongside, same era.
Tokui, S., Oono, K., Hido, S., Clayton, J.: Chainer: a next-generation open source framework for deep learning. In: Proceedings of workshop on machine learning systems (LearningSys) in the twenty-ninth annual conference on neural information processing systems (NIPS) (2015)
2015
Later among the works it cites.
2016
Later among the works it cites.
2016
Later among the works it cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2016)
2016
Later among the works it cites.
Mou, L., Li, G., Zhang, L., Wang, T., Jin, Z.: Convolutional neural networks over tree structures for programming language processing. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA. pp. 1287–1293 (2016)
2016
Later among the works it cites.
Polikarpova, N., Kuraj, I., Solar-Lezama, A.: Program synthesis from polymorphic refinement types. In: Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2016, Santa Barbara, CA, USA, June 13-17, 2016. pp. 522–538 (2016)
2016
Later among the works it cites.
2017
Closest in time.