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The field of neuro-symbolic artificial intelligence (NeSy), which combines learning and reasoning, has recently experienced significant growth.
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Marra, G., Giannini, F., Diligenti, M., Gori, M.: Lyrics: A general interface layer to integrate logic inference and deep learning. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. pp. 283–298. Springer (2019)
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Magnini, M., Ciatto, G., Omicini, A.: On the Design of PSyKI: A Platform for Symbolic Knowledge Injection into Sub-symbolic Predictors. In: Calvaresi, D., Najjar, A., Winikoff, M., Främling, K. (eds.) Explainable and Transparent AI and Multi-Agent Systems, vol. 13283, pp. 90–108. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-15565-9-6
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Tang, Z., Pei, S., Peng, X., Zhuang, F., Zhang, X., Hoehndorf, R.: TAR: Neural Logical Reasoning across TBox and ABox (Aug 2022)
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Winters, T., Marra, G., Manhaeve, R., De Raedt, L.: Deepstochlog: Neural stochastic logic programming. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 36, pp. 10090–10100 (2022)
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Yang, Z., Ishay, A., Lee, J.: NeurASP: Embracing neural networks into answer set programming. In: Bessiere, C. (ed.) Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20. pp. 1755–1762. International Joint Conferences on Artificial Intelligence Organization (Jul 2020). https://doi.org/10.24963/ijcai.2020/243
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Badreddine, S., d’Avila Garcez, A., Serafini, L., Spranger, M.: Logic Tensor Networks. Artificial Intelligence 303
2021
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Huang, J., Li, Z., Chen, B., Samel, K., Naik, M., Song, L., Si, X.: Scallop: From Probabilistic Deductive Databases to Scalable Differentiable Reasoning. In: Advances in Neural Information Processing Systems (May 2021)
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2021
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van Krieken, E., Thanapalasingam, T., Tomczak, J.M., van Harmelen, F., ten Teije, A.: A-nesi: A scalable approximate method for probabilistic neurosymbolic inference. In: Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. (eds.) Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023 (2023), http://papers.nips.cc/paper_files/paper/2023/hash/4d9944ab3330fe6af8efb9260aa9f307-Abstract-Conference.html
2023
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Maene, J., Raedt, L.D.: Soft-Unification in Deep Probabilistic Logic. In: Thirty-Seventh Conference on Neural Information Processing Systems (Nov 2023)
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Pryor, C., Dickens, C., Augustine, E., Albalak, A., Wang, W.Y., Getoor, L.: NeuPSL: Neural Probabilistic Soft Logic. In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. pp. 4145–4153. International Joint Conferences on Artificial Intelligence Organization, Macau, SAR China (Aug 2023). https://doi.org/10.24963/ijcai.2023/461
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Pryor, C., Dickens, C., Augustine, E., Albalak, A., Wang, W.Y., Getoor, L.: Neupsl: Neural probabilistic soft logic. In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI 2023, 19th-25th August 2023, Macao, SAR, China. pp. 4145–4153. ijcai.org (2023). https://doi.org/10.24963/IJCAI.2023/461, https://doi.org/10.24963/ijcai.2023/461
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Slusarz, N., Komendantskaya, E., Daggitt, M.L., Stewart, R., Stark, K.: Logic of differentiable logics: Towards a uniform semantics of dl. In: Proceedings of 24th International Conference on Logic. vol. 94, pp. 473–493 (2023)
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Umili, E., Capobianco, R., De Giacomo, G.: Grounding ltlf specifications in image sequences. In: Proceedings of the International Conference on Principles of Knowledge Representation and Reasoning. vol. 19, pp. 668–678 (2023)
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van Krieken, E., Thanapalasingam, T., Tomczak, J., van Harmelen, F., Ten Teije, A.: A-NeSI: A scalable approximate method for probabilistic neurosymbolic inference. In: Oh, A., Neumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. (eds.) Advances in Neural Information Processing Systems. vol. 36, pp. 24586–24609. Curran Associates, Inc. (2023)
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De Smet, L., Sansone, E., Zuidberg Dos Martires, P.: Differentiable sampling of categorical distributions using the catlog-derivative trick. Advances in Neural Information Processing Systems 36
2024
Closest in time.
Derkinderen, V., Manhaeve, R., Dos Martires, P.Z., De Raedt, L.: Semirings for probabilistic and neuro-symbolic logic programming. International Journal of Approximate Reasoning p. 109130 (2024)
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Closest in time.
Giunchiglia, E., Tatomir, A., Stoian, M.C., Lukasiewicz, T.: CCN+: A neuro-symbolic framework for deep learning with requirements. International Journal of Approximate Reasoning p. 109124 (2024). https://doi.org/10.1016/j.ijar.2024.109124
2024
Closest in time.