Fetching the paper…
Reading the bibliography…
Neuro-Symbolic Artificial Intelligence -- the combination of symbolic methods with methods that are based on artificial neural networks -- has a long-standing history.
M. Fischer, M. Balunovic, D. Drachsler-Cohen, T. Gehr, C. Zhang and M.T. Vechev, DL2: Training and Querying Neural Networks with Logic, in: Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA
1941
Earlier work this paper cites.
W.S. McCulloch and W. Pitts, A Logical Calculus of the Ideas Immanent in Nervous Activity, Bulletin of Mathematical Biophysics
1943
Earlier work this paper cites.
J. McCarthy, Epistomological challenges for connectionism, Behavioral and Brain Sciences
1988
Earlier work this paper cites.
L. Shastri, Advances in SHRUTI-A Neurally Motivated Model of Relational Knowledge Representation and Rapid Inference Using Temporal Synchrony, Appl. Intell
1999
Earlier work this paper cites.
P. Hitzler and A.K. Seda, Generalized metrics and uniquely determined logic programs, Theor. Comput. Sci
2003
Earlier work this paper cites.
S. Bader and P. Hitzler, Dimensions of Neural-symbolic Integration – A Structured Survey, in: We Will Show Them! Essays in Honour of Dov Gabbay, Volume One
2005
Earlier work this paper cites.
B. Hammer and P. Hitzler (eds), Perspectives of Neural-Symbolic Integration
2007
Earlier work this paper cites.
A.S. d’Avila Garcez, L.C. Lamb and D.M. Gabbay, Neural-Symbolic Cognitive Reasoning
2009
Earlier work this paper cites.
P. Hitzler, M. Krötzsch and S. Rudolph, Foundations of Semantic Web Technologies
2010
Earlier work this paper cites.
L. de Penning, A.S. d’Avila Garcez, L.C. Lamb and J.C. Meyer, A Neural-Symbolic Cognitive Agent for Online Learning and Reasoning, in: IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain, July 16-22, 2011
2011
Earlier work this paper cites.
S. Cao, W. Lu and Q. Xu, Deep Neural Networks for Learning Graph Representations, in: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA
2016
Earlier work this paper cites.
C. Xiong, S. Merity and R. Socher, Dynamic Memory Networks for Visual and Textual Question Answering, in: Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
L. Mou, Z. Lu, H. Li and Z. Jin, Coupling Distributed and Symbolic Execution for Natural Language Queries, in: Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017
2017
Earlier work this paper cites.
C. Xiao, M. Dymetman and C. Gardent, Symbolic Priors for RNN-based Semantic Parsing, in: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017
2017
Earlier work this paper cites.
E. Parisotto, A. Mohamed, R. Singh, L. Li, D. Zhou and P. Kohli, Neuro-Symbolic Program Synthesis, in: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings
2017
Earlier work this paper cites.
R. Trivedi, H. Dai, Y. Wang and L. Song, Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs, in: Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017
2017
Earlier work this paper cites.
M. Allamanis, P. Chanthirasegaran, P. Kohli and C. Sutton, Learning Continuous Semantic Representations of Symbolic Expressions, in: Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017
2017
Earlier work this paper cites.
I. Donadello, L. Serafini and A.S. d’Avila Garcez, Logic Tensor Networks for Semantic Image Interpretation, in: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017
2017
Earlier work this paper cites.
F. Yang, D. Lyu, B. Liu and S. Gustafson, PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making, in: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden
2018
Earlier work this paper cites.
R. Manhaeve, S. Dumancic, A. Kimmig, T. Demeester and L.D. Raedt, DeepProbLog: Neural Probabilistic Logic Programming, in: Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada
2018
Earlier work this paper cites.
F. Arabshahi, S. Singh and A. Anandkumar, Combining Symbolic Expressions and Black-box Function Evaluations in Neural Programs, in: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings
2018
Cited alongside, same era.
X. Liang, Z. Hu, H. Zhang, L. Lin and E.P. Xing, Symbolic Graph Reasoning Meets Convolutions, in: Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada
2018
Cited alongside, same era.
K. Yi, J. Wu, C. Gan, A. Torralba, P. Kohli and J. Tenenbaum, Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding, in: Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada
2018
Cited alongside, same era.
X. Chen, C. Liang, A.W. Yu, D. Song and D. Zhou, Compositional Generalization via Neural-Symbolic Stack Machines, in: Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual
2020
Later among the works it cites.
J. Jiang and S. Ahn, Generative Neurosymbolic Machines, in: Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual
2020
Later among the works it cites.
W.W. Cohen, H. Sun, R.A. Hofer and M. Siegler, Scalable Neural Methods for Reasoning With a Symbolic Knowledge Base, in: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020
2020
Later among the works it cites.
K.K. Teru, E. Denis and W. Hamilton, Inductive Relation Prediction by Subgraph Reasoning, in: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. Xu, Z. Zhang, T. Friedman, Y. Liang and G.V. den Broeck, A Semantic Loss Function for Deep Learning with Symbolic Knowledge, in: Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018
2018
Cited alongside, same era.
A. Santoro, F. Hill, D.G.T. Barrett, A.S. Morcos and T.P. Lillicrap, Measuring abstract reasoning in neural networks, in: Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018
2018
Cited alongside, same era.
M. Asai and A. Fukunaga, Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary, in: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018
2018
Cited alongside, same era.
X. Zhang, A. Solar-Lezama and R. Singh, Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections, in: Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada
2018
Cited alongside, same era.
J. Mao, C. Gan, P. Kohli, J.B. Tenenbaum and J. Wu, The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision, in: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019
2019
Cited alongside, same era.
D. Lyu, F. Yang, B. Liu and S. Gustafson, SDRL: Interpretable and Data-Efficient Deep Reinforcement Learning Leveraging Symbolic Planning, in: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019
2019
Cited alongside, same era.
Y. Xie, Z. Xu, K.S. Meel, M.S. Kankanhalli and H. Soh, Embedding Symbolic Knowledge into Deep Networks, in: Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada
2019
Cited alongside, same era.
A.M. Alaa and M. van der Schaar, Demystifying Black-box Models with Symbolic Metamodels, in: Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada
2019
Cited alongside, same era.
R. Vedantam, K. Desai, S. Lee, M. Rohrbach, D. Batra and D. Parikh, Probabilistic Neural Symbolic Models for Interpretable Visual Question Answering, in: Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA
2019
Cited alongside, same era.
2020
Later among the works it cites.
S. Garg, A. Bajpai and Mausam, Symbolic Network: Generalized Neural Policies for Relational MDPs, in: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event
2020
Later among the works it cites.
R. Dang-Nhu, PLANS: Neuro-Symbolic Program Learning from Videos, in: Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual
2020
Later among the works it cites.
X. Chen, C. Liang, A.W. Yu, D. Zhou, D. Song and Q.V. Le, Neural Symbolic Reader: Scalable Integration of Distributed and Symbolic Representations for Reading Comprehension, in: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020
2020
Later among the works it cites.
P. Hohenecker and T. Lukasiewicz, Ontology Reasoning with Deep Neural Networks, J. Artif. Intell. Res
2020
Later among the works it cites.
M. Asai and C. Muise, Learning Neural-Symbolic Descriptive Planning Models via Cube-Space Priors: The Voyage Home (to STRIPS), in: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020
2020
Later among the works it cites.
M.D. Cranmer, A. Sanchez-Gonzalez, P.W. Battaglia, R. Xu, K. Cranmer, D.N. Spergel and S. Ho, Discovering Symbolic Models from Deep Learning with Inductive Biases, in: Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual
2020
Later among the works it cites.
B. Dhingra, M. Zaheer, V. Balachandran, G. Neubig, R. Salakhutdinov and W.W. Cohen, Differentiable Reasoning over a Virtual Knowledge Base, in: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020
2020
Later among the works it cites.
S. Amizadeh, H. Palangi, A. Polozov, Y. Huang and K. Koishida, Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning", in: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event
2020
Later among the works it cites.
Q. Li, S. Huang, Y. Hong, Y. Chen, Y.N. Wu and S. Zhu, Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning, in: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event
2020
Later among the works it cites.
D. Chen, Y. Bai, W. Zhao, S. Ament, J.M. Gregoire and C.P. Gomes, Deep Reasoning Networks for Unsupervised Pattern De-mixing with Constraint Reasoning, in: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event
2020
Later among the works it cites.
P. Minervini, S. Riedel, P. Stenetorp, E. Grefenstette and T. Rocktäschel, Learning Reasoning Strategies in End-to-End Differentiable Proving, in: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event
2020
Later among the works it cites.
P. Hitzler, F. Bianchi, M. Ebrahimi and M.K. Sarker, Neural-symbolic integration and the Semantic Web, Semantic Web
2020
Later among the works it cites.
A. Eberhart, M. Ebrahimi, L. Zhou, C. Shimizu and P. Hitzler, Completion Reasoning Emulation for the Description Logic EL+, in: Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice, AAAI-MAKE 2020, Palo Alto, CA, USA, March 23-25, 2020, Volume I
2020
Later among the works it cites.
P. Hitzler, A review of the semantic web field, Commun. ACM
2021
Closest in time.
M. Ebrahimi, A. Eberhart, F. Bianchi and P. Hitzler, Towards Bridging the Neuro-Symbolic Gap: Deep Deductive Reasoners, Applied Intelligence
2021
Closest in time.
M. Ebrahimi, M.K. Sarker, F. Bianchi, N. Xie, A. Eberhart, D. Doran, H. Kim and P. Hitzler, Neuro-Symbolic Deductive Reasoning for Cross-Knowledge Graph Entailment, in: Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021), Stanford University, Palo Alto, California, USA, March 22-24, 2021
2021
Closest in time.