Fetching the paper…
Reading the bibliography…
For many reasoning-heavy tasks involving raw inputs, it is challenging to design an appropriate end-to-end learning pipeline.
Abductive logic programming
Antonis C. Kakas, Robert A. Kowalski, and Francesca Toni · 1992
Earlier work this paper cites.
Inductive logic programming: Theory and methods
Stephen H. Muggleton and Luc de Raedt · 1994
Earlier work this paper cites.
Abduction and Induction: Essays on Their Relation and Integration
Peter A. Flach, Antonis C. Kakas, and Antonis M. Hadjiantonis, editors · 2000
Earlier work this paper cites.
Thinking, fast and slow
Daniel Kahneman · 2011
Earlier work this paper cites.
MetaBayes: Bayesian meta-interpretative learning using higher-order stochastic refinement
Stephen H. Muggleton, Dianhuan Lin, Jianzhong Chen, and Alireza Tamaddoni-Nezhad · 2013
Earlier work this paper cites.
Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited
Stephen H. Muggleton, Dianhuan Lin, and Alireza Tamaddoni-Nezhad · 2015
Earlier work this paper cites.
Yoshua Bengio · 2017
Earlier work this paper cites.
Combining logical abduction and statistical induction: Discovering written primitives with human knowledge
W.-Z. Dai and Z.-H. Zhou · 2017
Earlier work this paper cites.
Logic tensor networks for semantic image interpretation
Ivan Donadello, Luciano Serafini, and Artur S. d’Avila Garcez · 2017
Earlier work this paper cites.
Differentiable programs with neural libraries
Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, and Daniel Tarlow · 2017
Earlier work this paper cites.
Limits of end-to-end learning
Tobias Glasmachers · 2017
Earlier work this paper cites.
Satisfiability modulo theories
Clark W. Barrett and Cesare Tinelli · 2018
Cited alongside, same era.
Learning explanatory rules from noisy data
Richard Evans and Edward Grefenstette · 2018
Cited alongside, same era.
How much can experimental cost be reduced in active learning of agent strategies?
Céline Hocquette and Stephen H. Muggleton · 2018
Cited alongside, same era.
Deepproblog: Neural probabilistic logic programming
Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, and Luc De Raedt · 2018
Cited alongside, same era.
Neural arithmetic logic units
Andrew Trask, Felix Hill, Scott E Reed, Jack Rae, Chris Dyer, and Phil Blunsom · 2018
Cited alongside, same era.
Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, and Maosong Sun · 2018
Stochastic optimization of sorting networks via continuous relaxations
Aditya Grover, Eric Wang, Aaron Zweig, and Stefano Ermon · 2019
Later among the works it cites.
Answer Set Programming
Vladimir Lifschitz · 2019
Later among the works it cites.
SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
Po-Wei Wang, Priya L. Donti, Bryan Wilder, and J. Zico Kolter · 2019
Later among the works it cites.
Abductive learning: towards bridging machine learning and logical reasoning
Zhi-Hua Zhou · 2019
Later among the works it cites.
Tensorlog: A probabilistic database implemented using deep-learning infrastructure
William W. Cohen, Fan Yang, and Kathryn Mazaitis · 2020
Closest in time.
Learning higher-order logic programs
Andrew Cropper, Rolf Morel, and Stephen Muggleton · 2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Learning efficient logic programs
Andrew Cropper and Stephen H. Muggleton · 2019
Cited alongside, same era.
Bridging machine learning and logical reasoning by abductive learning
Wang-Zhou Dai, Qiu-Ling Xu, Yang Yu, and Zhi-Hua Zhou · 2019
Cited alongside, same era.
Neural logic machines
Honghua Dong, Jiayuan Mao, Tian Lin, Chong Wang, Lihong Li, and Denny Zhou · 2019
Cited alongside, same era.
Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning
Artur S. d’Avila Garcez, Marco Gori, Luís C. Lamb, Luciano Serafini, Michael Spranger, and Son N. Tran · 2019
Cited alongside, same era.
From statistical relational to neuro-symbolic artificial intelligence
Luc De Raedt, Sebastijan Dumančić, Robin Manhaeve, and Giuseppe Marra · 2020
Closest in time.
Differentiable reasoning over a virtual knowledge base
Bhuwan Dhingra, Manzil Zaheer, Vidhisha Balachandran, Graham Neubig, Ruslan Salakhutdinov, and William W. Cohen · 2020
Closest in time.
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
Closest in time.
Making sense of raw input
Richard Evans, Matko Bošnjak, Lars Buesing, Kevin Ellis, David Pfau, Pushmeet Kohli, and Marek J. Sergot · 2021
Closest in time.