The list will be updated constantly throughout the semester. Papers are sorted by year instead of any particular importance.
Bill MacCartney and C. Manning Natural Logic for Textual Inference 2007
Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks 2016
Georgios P. Spithourakis and Sebastian Riedel Numeracy for Language Models: Evaluating and Improving their Ability to Predict Numbers 2018
Richard Evans, David Saxton, David Amos, Pushmeet Kohli, Edward Grefenstette Can Neural Networks Understand Logical Entailment? 2018
Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S. Du, Ken-ichi Kawarabayashi, Stefanie Jegelka What Can Neural Networks Reason About? 2019
David G.T. Barrett, Felix Hill, Adam Santoro, Ari S. Morcos, Timothy Lillicrap Measuring abstract reasoning in neural networks 2019
Tushar Khot, Ashish Sabharwal, Peter Clark What’s Missing: A Knowledge Gap Guided Approach for Multi-hop Question Answering 2019
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, Matt Gardner DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs 2019
Kevin Lin, Oyvind Tafjord, Peter Clark, Matt Gardner Reasoning Over Paragraph Effects in Situations 2020
John McCarthy Programs with Common Sense 1959
John McCarthy Some Philosophical Problems from the Standpoint of Artifical Intelligence 1969
John McCarthy AN EXAMPLE FOR NATURAL LANGUAGE UNDERSTANDING AND THE AI PROBLEMS IT RAISES 1976
Jerry R. Hobbs, Mark Stickel, Paul Martin, Douglas Edwards Interpretation as Abduction 1988
Bart Selman, Hector J. Levesque Abductive and Default Reasoning: A Computational Core 1990
Dan Roth Learning to Reason: The Approach 1996
P.N. Johnson-Laird, Vittorio Girotto, and Paolo Legrenzi Mental models: a gentle guide for outsiders 1998
Sarit Kraus, Daniel Lehmann, Menachem Magidor Nonmonotonic Reasoning, Preferential Models and Cumulative Logics 2002
Adnan Darwiche Modeling and Reasoning with Bayesian Networks 2009
Philip N. Johnson-Laird Mental models and human reasoning 2010
Brendan Juba Implicit Learning of Common Sense for Reasoning 2013
Brenden M. Lake, T. D. Ullman, J.B. Tenenbaum and J. Gershman Building Machines That Learn and Think Like People 2016
Brendan Juba Learning Abductive Reasoning Using Random Examples 2016
Vaishak Belle and Brendan Juba Implicitly Learning to Reason in First-Order Logic 2019
Ming-Wei Chang, Lev Ratinov, Nicholas Rizzolo, Dan Roth Learning and Inference with Constraints 2008
James Clarke, Dan Goldwasser, Ming-Wei Chang, Dan Roth Driving Semantic Parsing from the World’s Response 2010
Islam Beltagy et al. Montague Meets Markov: Deep Semantics with Probabilistic Logical Form StarSEM 2013
Alex Graves, Greg Wayne, Ivo Danihelka Neural Turing Machines 2014
Jason Weston, Sumit Chopra, Antoine Bordes Memory Networks 2014
Marta Garnelo, Kai Arulkumaran, Murray Shanahan Towards Deep Symbolic Reinforcement Learning 2016
Jacob Andreas et al. Learning to Compose Neural Networks for Question Answering 2016
Daniel Khashabi, Tushar Khot, Ashish Sabharwal, Peter Clark, Oren Etzioni, Dan Roth Question Answering via Integer Programming over Semi-Structured Knowledge 2016
Gabor Angeli, Neha Nayak, Christopher D. Manning Combining Natural Logic and Shallow Reasoning for Question Answering 2016
Zhiting Hu, Xuezhe Ma, Zhengzhong Liu, Eduard Hovy, Eric Xing Harnessing Deep Neural Networks with Logic Rules 2016
Fan Yang, Zhilin Yang, William W. Cohen Differentiable Learning of Logical Rules for Knowledge Base Reasoning 2016
Jingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang, Guy Van den Broeck A Semantic Loss Function for Deep Learning with Symbolic Knowledge 2017
Subhro Roy and Dan Roth Mapping to Declarative Knowledge for Word Problem Solving 2018
Kexin Yi, Jiajun Wu, Chuang Gan, Antonio Torralba, Pushmeet Kohli, Joshua B. Tenenbaum Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding 2018
Drew A. Hudson and C. Manning Compositional Attention Networks For Machine Reasoning 2018
Qiang Ning, Zhili Feng, Hao Wu, Dan Roth Joint Reasoning for Temporal and Causal Relations 2018
Nuri Cingillioglu, Alessandra Russo DeepLogic: Towards End-to-End Differentiable Logical Reasoning 2019
Tao Li, Vivek Srikumar Augmenting Neural Networks with First-order Logic 2019
Po-Wei Wang, Priya L. Donti, Bryan Wilder, Zico Kolter SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver 2019
Yatin Nandwani, Abhishek Pathak, Mausam, Parag Singla A Primal-Dual Formulation for Deep Learning with Constraints 2019
Pasquale Minervini, Matko Bošnjak, Tim Rocktäschel, Sebastian Riedel, Edward Grefenstette Differentiable Reasoning on Large Knowledge Bases and Natural Language 2019
Nitish Gupta, Kevin Lin, Dan Roth, Sameer Singh, Matt Gardner Neural Module Networks for Reasoning over Text 2020
Bhuwan Dhingra, Manzil Zaheer, Vidhisha Balachandran, Graham Neubig, Ruslan Salakhutdinov, William W. Cohen DIFFERENTIABLE REASONING OVER A VIRTUAL KNOWLEDGE BASE 2020
Ziqi Wang, Yujia Qin, Wenxuan Zhou, Jun Yan, Qinyuan Ye, Leonardo Neves, Zhiyuan Liu, Xiang Ren Learning from Explanations with Neural Execution Tree 2020
Luis Lamb, Artur Garcez, Marco Gori, Marcelo Prates, Pedro Avelar, Moshe Vardi Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective 2020