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Neuro-Symbolic (NeSy) predictive models hold the promise of improved compliance with given constraints, systematic generalization, and interpretability, as they allow to infer labels that are consistent with some prior knowledge by reasoning over high-level concepts extracted from sub-symbolic inputs.
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Closed loop neural-symbolic learning via integrating neural perception, grammar parsing, and symbolic reasoning
Qing Li, Siyuan Huang, Yining Hong, Yixin Chen, Ying Nian Wu, and Song-Chun Zhu · 2020
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Calibrating deep neural networks using focal loss
Neuro-symbolic verification of deep neural networks
Xuan Xie, Kristian Kersting, and Daniel Neider · 2022
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Neupsl: Neural probabilistic soft logic
Connor Pryor, Charles Dickens, Eriq Augustine, Alon Albalak, William Wang, and Lise Getoor · 2022
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DeepStochLog: Neural Stochastic Logic Programming
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Explainable object-induced action decision for autonomous vehicles
Yiran Xu, Xiaoyin Yang, Lihang Gong, Hsuan-Chu Lin, Tz-Ying Wu, Yunsheng Li, and Nuno Vasconcelos · 2020
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Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann · 2020
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Noise or signal: The role of image backgrounds in object recognition
Kai Yuanqing Xiao, Logan Engstrom, Andrew Ilyas, and Aleksander Madry · 2020
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Making deep neural networks right for the right scientific reasons by interacting with their explanations
Patrick Schramowski, Wolfgang Stammer, Stefano Teso, Anna Brugger, Franziska Herbert, Xiaoting Shao, Hans-Georg Luigs, Anne-Katrin Mahlein, and Kristian Kersting · 2020
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Learning explanations that are hard to vary
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Variational autoencoders and nonlinear ICA: A unifying framework
Ilyes Khemakhem, Diederik Kingma, Ricardo Monti, and Aapo Hyvarinen · 2020
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Glancenets: Interpretabile, leak-proof concept-based models
Emanuele Marconato, Andrea Passerini, and Stefano Teso · 2022
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VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming
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Mitigating neural network overconfidence with logit normalization
Hongxin Wei, Renchunzi Xie, Hao Cheng, Lei Feng, Bo An, and Yixuan Li · 2022
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On mixup regularization
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Concept bottleneck model with additional unsupervised concepts
Yoshihide Sawada and Keigo Nakamura · 2022
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Function classes for identifiable nonlinear independent component analysis
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On the relationship between disentanglement and multi-task learning
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Deep symbolic learning: Discovering symbols and rules from perceptions
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Mateo Espinosa Zarlenga, Pietro Barbiero, Gabriele Ciravegna, Giuseppe Marra, Francesco Giannini, Michelangelo Diligenti, Zohreh Shams, Frederic Precioso, Stefano Melacci, Adrian Weller, et al · 2022
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LTNtorch: PyTorch implementation of Logic Tensor Networks, mar 2022
Tommaso Carraro · 2022
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Learning with logical constraints but without shortcut satisfaction
Zenan Li, Zehua Liu, Yuan Yao, Jingwei Xu, Taolue Chen, Xiaoxing Ma, L Jian, et al · 2023
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Nonlinear independent component analysis for principled disentanglement in unsupervised deep learning
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Interventional causal representation learning
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Nonparametric identifiability of causal representations from unknown interventions
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Leveraging sparse and shared feature activations for disentangled representation learning
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