[42] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. In International conference on machine learning, 2014.
[43] Partha Ghosh, Mehdi SM Sajjadi, Antonio Vergari, Michael J Black, and Bernhard Schölkopf. From variational to deterministic autoencoders. In ICLR, 2020.
[44] Eleonora Misino, Giuseppe Marra, and Emanuele Sansone. VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming. NeurIPS, 2022.
[45] Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem. Challenging common assumptions in the unsupervised learning of disentangled representations. In ICML, 2019.
[46] Francesco Locatello, Ben Poole, Gunnar Ratsch, Bernhard Scholkopf, Olivier Bachem, and Michael Tschannen. Weakly-supervised disentanglement without compromises. In International Conference on Machine Learning, pages 6348-6359. PMLR, 2020.
[47] Rui Shu, Yining Chen, Abhishek Kumar, Stefano Ermon, and Ben Poole. Weakly supervised disentanglement with guarantees. In ICLR, 2019.
[48] Rafael Müller, Simon Kornblith, and Geoffrey E Hinton. When does label smoothing help? Advances in neural information processing systems, 32, 2019.
[49] Shuang Li, Yilun Du, Gido Martijn van de Ven, and Igor Mordatch. Energy-based models for continual learning. In Energy Based Models Workshop-ICLR 2021, 2021.
[50] Qing Li, Siyuan Huang, Yining Hong, Yixin Chen, Ying Nian Wu, and Song-Chun Zhu. Closed loop neural-symbolic learning via integrating neural perception, grammar parsing, and symbolic reasoning. In International Conference on Machine Learning, pages 5884-5894. PMLR, 2020.
[51] Hongxin Wei, Renchunzi Xie, Hao Cheng, Lei Feng, Bo An, and Yixuan Li. Mitigating neural network overconfidence with logit normalization. In International Conference on Machine Learning, pages 23631-23644. PMLR, 2022.
[52] Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip Torr, and Puneet Dokania. Calibrating deep neural networks using focal loss. Advances in Neural Information Processing Systems, 33:15288-15299, 2020.
[53] Luigi Carratino, Moustapha Cisse, Rodolphe Jenatton, and Jean-Philippe Vert. On mixup regularization. The Journal of Machine Learning Research, 23(1):14632-14662, 2022.
[54] Yiran Xu, Xiaoyin Yang, Lihang Gong, Hsuan-Chu Lin, Tz-Ying Wu, Yunsheng Li, and Nuno Vasconcelos. Explainable object-induced action decision for autonomous vehicles. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020.
[55] Yoshihide Sawada and Keigo Nakamura. Concept bottleneck model with additional unsupervised concepts. IEEE Access, 10:41758-41765, 2022.
[56] Robert Geirhos, Jorn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann. Shortcut learning in deep neural networks. Nature Machine Intelligence, 2(11):665-673, 2020.
[57] Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder, Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller. Unmasking clever hans predictors and assessing what machines really learn. Nature communications, 10(1):1-8, 2019.
[58] Kai Yuanqing Xiao, Logan Engstrom, Andrew Ilyas, and Aleksander Madry. Noise or signal: The role of image backgrounds in object recognition. In ICLR, 2020.
[59] Patrick Schramowski, Wolfgang Stammer, Stefano Teso, Anna Brugger, Franziska Herbert, Xiaoting Shao, Hans-Georg Luigs, Anne-Katrin Mahlein, and Kristian Kersting. Making deep neural networks right for the right scientific reasons by interacting with their explanations. Nature Machine Intelligence, 2(8):476–486, 2020.