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Deep learning is very effective at jointly learning feature representations and classification models, especially when dealing with high dimensional input patterns.
Quinlan, J.R.: Learning logical definitions from relations. Machine learning 5(3), 239–266 (1990)
1990
Earlier work this paper cites.
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall PTR, Upper Saddle River, NJ, USA, 1st edn. (1994)
1994
Earlier work this paper cites.
Lavrac, N., Dzeroski, S.: Inductive logic programming. In: WLP. pp. 146–160. Springer (1994)
1994
Earlier work this paper cites.
Muggleton, S., De Raedt, L.: Inductive logic programming: Theory and methods. The Journal of Logic Programming 19, 629–679 (1994)
1994
Earlier work this paper cites.
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998)
1998
Earlier work this paper cites.
Richardson, M., Domingos, P.: Markov logic networks. Machine learning 62(1-2), 107–136 (2006)
2006
Earlier work this paper cites.
De Raedt, L., Kimmig, A., Toivonen, H.: Problog: A probabilistic prolog and its application in link discovery. In: Proceedings of the 20th International Joint Conference on Artifical Intelligence. pp. 2468–2473. IJCAI’07, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2007), http://dl.acm.org/citation.cfm?id=1625275.1625673
2007
Earlier work this paper cites.
Bengio, Y., et al.: Learning deep architectures for ai. Foundations and trends® in Machine Learning 2(1), 1–127 (2009)
2009
Earlier work this paper cites.
Garcez, A.S.d., Broda, K.B., Gabbay, D.M.: Neural-symbolic learning systems: foundations and applications. Springer Science & Business Media (2012)
2012
Earlier work this paper cites.
Kimmig, A., Bach, S., Broecheler, M., Huang, B., Getoor, L.: A short introduction to probabilistic soft logic. In: Proceedings of the NIPS Workshop on Probabilistic Programming: Foundations and Applications. pp. 1–4 (2012)
2012
Earlier work this paper cites.
Novák, V., Perfilieva, I., Mockor, J.: Mathematical principles of fuzzy logic, vol. 517. Springer Science & Business Media (2012)
2012
Cited alongside, same era.
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in neural information processing systems. pp. 2787–2795 (2013)
2013
Cited alongside, same era.
2015
Cited alongside, same era.
Bouchard, G., Singh, S., Trouillon, T.: On approximate reasoning capabilities of low-rank vector spaces. AAAI Spring Syposium on Knowledge Representation and Reasoning (KRR): Integrating Symbolic and Neural Approaches (2015)
2015
Cited alongside, same era.
Ma, X., Hovy, E.: End-to-end sequence labeling via bi-directional lstm-cnns-crf. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 1064–1074. Association for Computational Linguistics (2016), http://aclweb.org/anthology/P16-1101
2016
Later among the works it cites.
Rocktäschel, T., Riedel, S.: Learning knowledge base inference with neural theorem provers. In: Proceedings of the 5th Workshop on Automated Knowledge Base Construction. pp. 45–50 (2016)
2016
Later among the works it cites.
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G.: Complex embeddings for simple link prediction. In: International Conference on Machine Learning. pp. 2071–2080 (2016)
2016
Later among the works it cites.
Diligenti, M., Gori, M., Sacca, C.: Semantic-based regularization for learning and inference. Artificial Intelligence 244, 143–165 (2017)
2017
Later among the works it cites.
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Chen, L.C., Schwing, A., Yuille, A., Urtasun, R.: Learning deep structured models. In: International Conference on Machine Learning. pp. 1785–1794 (2015)
2015
Cited alongside, same era.
Mahendran, A., Vedaldi, A.: Understanding deep image representations by inverting them. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 5188–5196 (2015)
2015
Cited alongside, same era.
Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep learning, vol. 1. MIT press Cambridge (2016)
2016
Cited alongside, same era.
Hazan, T., Schwing, A.G., Urtasun, R.: Blending learning and inference in conditional random fields. The Journal of Machine Learning Research 17(1), 8305–8329 (2016)
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Lin, G., Shen, C., Van Den Hengel, A., Reid, I.: Efficient piecewise training of deep structured models for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3194–3203 (2016)
2016
Cited alongside, same era.
2017
Later among the works it cites.
Rocktäschel, T., Riedel, S.: End-to-end differentiable proving. In: Advances in Neural Information Processing Systems. pp. 3788–3800 (2017)
2017
Later among the works it cites.
2018
Later among the works it cites.
Giannini, F., Diligenti, M., Gori, M., Maggini, M.: On a convex logic fragment for learning and reasoning. IEEE Transactions on Fuzzy Systems (2018)
2018
Later among the works it cites.