[47] Subham Sekhar Sahoo, Anselm Paulus, Marin Vlastelica, Vít Musil, Volodymyr Kuleshov, and Georg Martius. Backpropagation through Combinatorial Algorithms: Identity with Projection Works. In The Eleventh International Conference on Learning Representations, February 2023.
[48] Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling. Modeling relational data with graph convolutional networks. In Aldo Gangemi, Roberto Navigli, Maria-Esher Vidal, Pascal Hitzler, Raphaël Troncy, Laura Hollink, Anna Tordai, and Mehwish Alam, editors, The Semantic Web, pages 593-607, Cham, 2018. Springer International Publishing. ISBN 978-3-319-93417-4.
[49] John Schulman, Nicolas Heess, Theophane Weber, and Pieter Abbeel. Gradient estimation using stochastic computation graphs. In Advances in Neural Information Processing Systems, 2015.
[50] Dimitar Shterionov, Joris Renkens, Jonas Vlasselaer, Angelika Kimmig, Wannes Meert, and Gerda Janssens. The Most Probable Explanation for Probabilistic Logic Programs with Annotated Disjunctions. In Jesse Davis and Jan Ramon, editors, Inductive Logic Programming, Lecture Notes in Computer Science, pages 139-153, Cham, 2015. Springer International Publishing. ISBN 978-3-319-23708-4. doi: 10.1007/978-3-319-23708-4_10.
[51] Jakub M. Tomczak. Deep Generative Modeling. Springer International Publishing, Cham, 2022. ISBN 978-3-030-93157-5 978-3-030-93158-2. doi: 10.1007/978-3-030-93158-2.
[52] Frank van Harmelen and Annette ten Teije. A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems. Journal of Web Engineering, 18(1):97-124, 2019. ISSN 1540-9589. doi: 10.13052/jwe1540-9589.18133.
[53] Emile van Krieken, Jakub Tomczak, and Annette Ten Teije. Storchastic: A framework for general stochastic automatic differentiation. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, volume 34, pages 7574-7587. Curran Associates, Inc., 2021.
[54] Emile van Krieken, Erman Acar, and Frank van Harmelen. Analyzing differentiable fuzzy logic operators. Artificial Intelligence, 302:103602, 2022. ISSN 0004-3702. doi: 10.1016/j.artint.2021.103602.
[55] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention Is All You Need, December 2017.
[56] Thomas Winters, Giuseppe Marra, Robin Manhaeve, and Luc De Raedt. DeepStochLog: Neural Stochastic Logic Programming. In Proceedings of the First MiniCon Conference, February 2022.
[57] Jingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang, and Guy den Broeck. A semantic loss function for deep learning with symbolic knowledge. In Jennifer Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning, volume 80, pages 5502-5511, Stockholm, Sweden, 2018. PMLR.
[58] Zhun Yang, Adam Ishay, and Joohyung Lee. NeurASP: Embracing neural networks into answer set programming. In Christian Bessiere, editor, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20, pages 1755–1762. International Joint Conferences on Artificial Intelligence Organization, July 2020. doi: 10.24963/ijcai.2020/243.
[59] Chih-Kuan Yeh, Been Kim, Sercan O. Arik, Chun-Liang Li, Tomas Pfister, and Pradeep Ravikumar. On Completeness-aware Concept-Based Explanations in Deep Neural Networks, February 2022.
[60] Choi Yoojung, Antonio Vergari, and Guy Van den Broeck. Probabilistic circuits: A unifying framework for tractable probabilistic models. 2020.