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Reasoning is an essential part of human intelligence and thus has been a long-standing goal in artificial intelligence research.
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John W. Lloyd · 1984
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Fred D Miller · 1984
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JM Corchado · 1995
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Thomas Lukasiewicz · 1998
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Florentino Fdez-Riverola, Juan M Corchado, and Jesús M Torres · 2002
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Probabilistic logic learning
Luc De Raedt and Kristian Kersting · 2003
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Neuro-symbolic system for business internal control
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Iq tests are not for machines, yet
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From machine learning to machine reasoning
Léon Bottou · 2014
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Probabilistic (logic) programming concepts
Luc De Raedt and Angelika Kimmig · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross B. Girshick, and Jian Sun · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Harnessing deep neural networks with logic rules
Zhiting Hu, Xuezhe Ma, Zhengzhong Liu, Eduard Hovy, and Eric Xing · 2016
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Statistical relational artificial intelligence: Logic, probability, and computation
Luc De Raedt, Kristian Kersting, Sriraam Natarajan, and David Poole · 2016
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You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi · 2016
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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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End-to-end Differentiable Proving
Tim Rocktäschel and Sebastian Riedel · 2017
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Differentiable learning of logical rules for knowledge base reasoning
Fan Yang, Zhilin Yang, and William W. Cohen · 2017
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Learning explanatory rules from noisy data
Richard Evans and Edward Grefenstette · 2018
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
Jiayuan Mao, Chuang Gan, Pushmeet Kohli, Joshua B. Tenenbaum, and Jiajun Wu · 2019
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Kandinsky patterns, 2019
Heimo Mueller and Andreas Holzinger · 2019
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Synthesizing datalog programs using numerical relaxation
Xujie Si, Mukund Raghothaman, Kihong Heo, and Mayur Naik · 2019
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Neuro-symbolic visual reasoning: Disentangling “visual” from “reasoning”
Saeed Amizadeh, Hamid Palangi, Oleksandr Polozov, Yichen Huang, and Kazuhito Koishida · 2020
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Tensorlog: A probabilistic database implemented using deep-learning infrastructure
William W. Cohen, Fan Yang, and Kathryn Mazaitis · 2020
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Genesis: Generative scene inference and sampling with object-centric latent representations
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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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Lifted relational neural networks: Efficient learning of latent relational structures
Gustav Šourek, Vojtěch Aschenbrenner, Filip Železný, Steven Schockaert, and Ondřej Kuželka · 2018
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A semantic loss function for deep learning with symbolic knowledge
Jingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang, and Guy Van den Broeck · 2018
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Monet: Unsupervised scene decomposition and representation
Christopher P Burgess, Loic Matthey, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matt Botvinick, and Alexander Lerchner · 2019
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Bridging machine learning and logical reasoning by abductive learning
Wang-Zhou Dai, Qiuling Xu, Yang Yu, and Zhi-Hua Zhou · 2019
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Neuro-symbolic= neural+ logical+ probabilistic
Luc De Raedt, Robin Manhaeve, Sebastijan Dumancic, Thomas Demeester, and Angelika Kimmig · 2019
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Martin Engelcke, Adam R. Kosiorek, Oiwi Parker Jones, and Ingmar Posner · 2020
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Neuro-symbolic visual reasoning for multimedia event processing: Overview, prospects and challenges
Muhammad Jaleed Khan and Edward Curry · 2020
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Object-centric learning with slot attention
Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf · 2020
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Learning object-centric representations of multi-object scenes from multiple views
Li Nanbo, Cian Eastwood, and Robert B Fisher · 2020
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Ryan Riegel, Alexander Gray, Francois Luus, Naweed Khan, Ndivhuwo Makondo, Ismail Yunus Akhalwaya, Haifeng Qian, Ronald Fagin, Francisco Barahona, Udit Sharma, et al · 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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Conversational neuro-symbolic commonsense reasoning
Forough Arabshahi, Jennifer Lee, Mikayla Gawarecki, Kathryn Mazaitis, Amos Azaria, and Tom Mitchell · 2021
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Grounding physical concepts of objects and events through dynamic visual reasoning
Zhenfang Chen, Jiayuan Mao, Jiajun Wu, Kwan-Yee Kenneth Wong, Joshua B. Tenenbaum, and Chuang Gan · 2021
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Generalization and robustness implications in object-centric learning
Andrea Dittadi, Samuele Papa, Michele De Vita, Bernhard Schölkopf, Ole Winther, and Francesco Locatello · 2021
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Andreas Holzinger, Anna Saranti, and Heimo Mueller · 2021
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Differentiable inductive logic programming for structured examples
Hikaru Shindo, Masaaki Nishino, and Akihiro Yamamoto · 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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