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The advent of powerful neural classifiers has increased interest in problems that require both learning and reasoning.
The symbol grounding problem
Stevan Harnad · 1990
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Multitask learning
Rich Caruana · 1997
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The mnist database of handwritten digits
Yann LeCun · 1998
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Satlib: An online resource for research on sat
Holger H Hoos and Thomas Stützle · 2000
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Independent component analysis: algorithms and applications
Aapo Hyvärinen and Erkki Oja · 2000
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Prediction of protein β \beta -residue contacts by markov logic networks with grounding-specific weights
Marco Lippi and Paolo Frasconi · 2009
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Handbook of satisfiability
Armin Biere, Marijn Heule, and Hans van Maaren · 2009
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Probabilistic graphical models: principles and techniques
Daphne Koller and Nir Friedman · 2009
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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Sdd: A new canonical representation of propositional knowledge bases
Adnan Darwiche · 2011
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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Probabilistic (logic) programming concepts
Luc De Raedt and Angelika Kimmig · 2015
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Pyeda: Data structures and algorithms for electronic design automation
Chris Drake · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Deep residual learning for image recognition, 2015
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Learning knowledge base inference with neural theorem provers
Tim Rocktäschel and Sebastian Riedel · 2016
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Algorithmic improvements in approximate counting for probabilistic inference: From linear to logarithmic sat calls
Supratik Chakraborty, Kuldeep S. Meel, and Moshe Y. Vardi · 2016
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Semantic-based regularization for learning and inference
Michelangelo Diligenti, Marco Gori, and Claudio Sacca · 2017
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Logic tensor networks for semantic image interpretation
Ivan Donadello, Luciano Serafini, and Artur D’Avila Garcez · 2017
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CLEVR: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens Van Der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick · 2017
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A semantic loss function for deep learning with symbolic knowledge
Jingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang, and Guy Broeck · 2018
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DeepProbLog: Neural Probabilistic Logic Programming
Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, and Luc De Raedt · 2018
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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, et al · 2018
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Blender - a 3D modelling and rendering package
Blender Online Community · 2018
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A framework for the quantitative evaluation of disentangled representations
Cian Eastwood and Christopher KI Williams · 2018
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Embedding symbolic knowledge into deep networks
Yaqi Xie, Ziwei Xu, Mohan S Kankanhalli, Kuldeep S Meel, and Harold Soh · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
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Abductive learning: towards bridging machine learning and logical reasoning
Zhi-Hua Zhou · 2019
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Efficient generation of structured objects with constrained adversarial networks
Luca Di Liello, Pierfrancesco Ardino, Jacopo Gobbi, Paolo Morettin, Stefano Teso, and Andrea Passerini · 2020
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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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Concept whitening for interpretable image recognition
Zhi Chen, Yijie Bei, and Cynthia Rudin · 2020
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NeurASP: Embracing neural networks into answer set programming
Zhun Yang, Adam Ishay, and Joohyung Lee · 2020
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Abductive knowledge induction from raw data
Wang-Zhou Dai and Stephen H Muggleton · 2020
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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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Concept bottleneck models
Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang · 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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Bdd100k: A diverse driving dataset for heterogeneous multitask learning
Fisher Yu, Haofeng Chen, Xin Wang, Wenqi Xian, Yingying Chen, Fangchen Liu, Vashisht Madhavan, and Trevor Darrell · 2020
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From statistical relational to neural-symbolic artificial intelligence
Luc De Raedt, Sebastijan Dumančić, Robin Manhaeve, and Giuseppe Marra · 2021
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Scallop: From probabilistic deductive databases to scalable differentiable reasoning
Jiani Huang, Ziyang Li, Binghong Chen, Karan Samel, Mayur Naik, Le Song, and Xujie Si · 2021
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Neural markov logic networks
Giuseppe Marra and Ondřej Kuželka · 2021
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Neural probabilistic logic programming in deepproblog
Robin Manhaeve, Sebastijan Dumančić, Angelika Kimmig, Thomas Demeester, and Luc De Raedt · 2021
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Measuring mathematical problem solving with the math dataset
Neuro symbolic continual learning: Knowledge, reasoning shortcuts and concept rehearsal
Emanuele Marconato, Gianpaolo Bontempo, Elisa Ficarra, Simone Calderara, Andrea Passerini, and Stefano Teso · 2023
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On learning latent models with multi-instance weak supervision
Kaifu Wang, Efi Tsamoura, and Dan Roth · 2023
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Not all neuro-symbolic concepts are created equal: Analysis and mitigation of reasoning shortcuts
Emanuele Marconato, Stefano Teso, Antonio Vergari, and Andrea Passerini · 2023
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Concept-based explainable artificial intelligence: A survey
Eleonora Poeta, Gabriele Ciravegna, Eliana Pastor, Tania Cerquitelli, and Elena Baralis · 2023
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Distribution-aware neuro-symbolic verification
Faried Abu Zaid, Dennis Diekmann, and Daniel Neider · 2023
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Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt · 2021
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Toward causal representation learning
Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Kandinsky patterns
Heimo Müller and Andreas Holzinger · 2021
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Right for the right concept: Revising neuro-symbolic concepts by interacting with their explanations
Wolfgang Stammer, Patrick Schramowski, and Kristian Kersting · 2021
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Model counting
Carla P Gomes, Ashish Sabharwal, and Bart Selman · 2021
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Causal abstractions of neural networks
Atticus Geiger, Hanson Lu, Thomas Icard, and Christopher Potts · 2021
Cited alongside, same era.
Concept-level debugging of part-prototype networks
Andrea Bontempelli, Stefano Teso, Fausto Giunchiglia, and Andrea Passerini · 2023
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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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Learning symbolic representations through joint generative and discriminative training
Emanuele Sansone and Robin Manhaeve · 2023
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An experimental overview of neural-symbolic systems
Arne Vermeulen, Robin Manhaeve, and Giuseppe Marra · 2023
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Metamath: Bootstrap your own mathematical questions for large language models
Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T Kwok, Zhenguo Li, Adrian Weller, and Weiyang Liu · 2023
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Mammoth: Building math generalist models through hybrid instruction tuning
Xiang Yue, Xingwei Qu, Ge Zhang, Yao Fu, Wenhao Huang, Huan Sun, Yu Su, and Wenhu Chen · 2023
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Soft-unification in deep probabilistic logic
Jaron Maene and Luc De Raedt · 2023
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Neural probabilistic logic programming in discrete-continuous domains
Lennert De Smet, Pedro Zuidberg Dos Martires, Robin Manhaeve, Giuseppe Marra, Angelika Kimmig, and Luc De Readt · 2023
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Does a neural network really encode symbolic concepts?
Mingjie Li and Quanshi Zhang · 2023
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Probabilistic concept bottleneck models
Eunji Kim, Dahuin Jung, Sangha Park, Siwon Kim, and Sungroh Yoon · 2023
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Interpretable neural-symbolic concept reasoning
Pietro Barbiero, Gabriele Ciravegna, Francesco Giannini, Mateo Espinosa Zarlenga, Lucie Charlotte Magister, Alberto Tonda, Pietro Lió, Frederic Precioso, Mateja Jamnik, and Giuseppe Marra · 2023
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Interpretability is in the mind of the beholder: A causal framework for human-interpretable representation learning
Emanuele Marconato, Andrea Passerini, and Stefano Teso · 2023
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Towards automated circuit discovery for mechanistic interpretability
Arthur Conmy, Augustine Mavor-Parker, Aengus Lynch, Stefan Heimersheim, and Adrià Garriga-Alonso · 2023
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Causal abstraction for faithful model interpretation
Atticus Geiger, Chris Potts, and Thomas Icard · 2023
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Synergies between disentanglement and sparsity: Generalization and identifiability in multi-task learning
Sebastien Lachapelle, Tristan Deleu, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio, Simon Lacoste-Julien, and Quentin Bertrand · 2023
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Leveraging sparse and shared feature activations for disentangled representation learning, 2023
Marco Fumero, Florian Wenzel, Luca Zancato, Alessandro Achille, Emanuele Rodolà, Stefano Soatto, Bernhard Schölkopf, and Francesco Locatello · 2023
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Road-r: The autonomous driving dataset with logical requirements
Eleonora Giunchiglia, Mihaela Cătălina Stoian, Salman Khan, Fabio Cuzzolin, and Thomas Lukasiewicz · 2023
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Label-free concept bottleneck models
Tuomas Oikarinen, Subhro Das, Lam M Nguyen, and Tsui-Wei Weng · 2023
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Umili, E., Capobianco, R., & De Giacomo, G. (2023). Grounding LTLf specifications in image sequences. In Proceedings of the International Conference on Principles of Knowledge Representation and Reasoning
2023
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The role of foundation models in neuro-symbolic learning and reasoning
Daniel Cunnington, Mark Law, Jorge Lobo, and Alessandra Russo · 2024
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BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts
Emanuele Marconato, Samuele Bortolotti, Emile van Krieken, Antonio Vergari, Andrea Passerini, and Stefano Teso · 2024
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Ambiguity-aware abductive learning
Hao-Yuan He, Hui Sun, Zheng Xie, and Ming Li · 2024
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Soft-unification in deep probabilistic logic
Jaron Maene and Luc De Raedt · 2024
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The clock and the pizza: Two stories in mechanistic explanation of neural networks
Ziqian Zhong, Ziming Liu, Max Tegmark, and Jacob Andreas · 2024
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Causal component analysis
Liang Wendong, Armin Kekić, Julius von Kügelgen, Simon Buchholz, Michel Besserve, Luigi Gresele, and Bernhard Schölkopf · 2024
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Fan Shi, Bin Li, and Xiangyang Xue · 2024
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The kandy benchmark: Incremental neuro-symbolic learning and reasoning with kandinsky patterns
Luca Salvatore Lorello, Marco Lippi, and Stefano Melacci · 2024
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Uller: A unified language for learning and reasoning
Emile van Krieken, Samy Badreddine, Robin Manhaeve, and Eleonora Giunchiglia · 2024
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