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Recent work on neuro-symbolic inductive logic programming has led to promising approaches that can learn explanatory rules from noisy, real-world data.
The double description method
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Monoidal t-norm based logic: Towards a logic for left-continuous t-norms
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Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Getoor, L.; and Taskar, B. 2007 · 2007
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Statistical predicate invention
Kok, S.; and Domingos, P. 2007 · 2007
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Convolutional deep belief networks on CIFAR-10
Krizhevsky, A. 2010 · 2010
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Rectified Linear Units Improve Restricted Boltzmann Machines
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Translating embeddings for modeling multi-relational data
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Maxout Networks
Goodfellow, I.; Warde-Farley, D.; Mirza, M.; Courville, A.; and Bengio, Y. 2013 · 2013
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Meta-interpretive Learning: Application to Grammatical Inference
Muggleton, S. H.; Lin, D.; Pahlavi, N.; and Tamaddoni-Nezhad, A. 2014 · 2014
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On Approximate Reasoning Capabilities of Low-Rank Vector Spaces
Bouchard, G.; Singh, S.; and Trouillon, T. 2015 · 2015
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Observed versus latent features for knowledge base and text inference
Toutanova, K.; and Chen, D. 2015 · 2015
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Holographic Embeddings of Knowledge Graphs
Nickel, M.; Rosasco, L.; and Poggio, T. 2016 · 2016
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Logic Tensor Networks for Semantic Image Interpretation
Donadello, I.; Serafini, L.; and d’Avila Garcez, A. S. 2017 · 2017
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End-to-End Differentiable Proving
Rocktäschel, T.; and Riedel, S. 2017 · 2017
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Stacked Structure Learning for Lifted Relational Neural Networks
Sourek, G.; Svatos, M.; Zelezny, F.; Schockaert, S.; and Kuzelka, O. 2017 · 2017
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Learning Explanatory Rules from Noisy Data
Evans, R.; and Grefenstette, E. 2018 · 2018
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RelNN: A Deep Neural Model for Relational Learning
Kazemi, S. M.; and Poole, D. 2018 · 2018
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DeepProbLog: Neural Probabilistic Logic Programming
Manhaeve, R.; Dumancic, S.; Kimmig, A.; Demeester, T.; and Raedt, L. D. 2018 · 2018
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Neural Logic Machines
Dong, H.; Mao, J.; Lin, T.; Wang, C.; Li, L.; and Zhou, D. 2019 · 2019
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DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs
Sadeghian, A.; Armandpour, M.; Ding, P.; and Wang, D. Z. 2019 · 2019
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Homogeneous Linear Inequality Constraints for Neural Network Activations
Frerix, T.; Nießner, M.; and Cremers, D. 2020 · 2020
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Differentiable Learning of Logical Rules for Knowledge Base Reasoning
Yang, F.; Yang, Z.; and Cohen, W. W. 2017 · 2017
Cited alongside, same era.
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning
Das, R.; Dhuliawala, S.; Zaheer, M.; Vilnis, L.; Durugkar, I.; Krishnamurthy, A.; Smola, A.; and McCallum, A. 2018 · 2018
Cited alongside, same era.
Convolutional 2d knowledge graph embeddings
Dettmers, T.; Minervini, P.; Stenetorp, P.; and Riedel, S. 2018 · 2018
Cited alongside, same era.
Differentiable reasoning on large knowledge bases and natural language
Minervini, P.; Bosnjak, M.; Rocktäschel, T.; Riedel, S.; and Grefenstette, E. 2020a
Cited in the paper.
Learning Reasoning Strategies in End-to-End Differentiable Proving
Minervini, P.; Riedel, S.; Stenetorp, P.; Grefenstette, E.; and Rocktäschel, T. 2020b
Cited in the paper.
Logical Neural Networks
Riegel, R.; Gray, A.; Luus, F.; Khan, N.; Makondo, N.; Akhalwaya, I. Y.; Qian, H.; Fagin, R.; Barahona, F.; Sharma, U.; Ikbal, S.; Karanam, H.; Neelam, S.; Likhyani, A.; and Srivastava, S. 2020 · 2020
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A Re-evaluation of Knowledge Graph Completion Methods
Sun, Z.; Vashishth, S.; Sanyal, S.; Talukdar, P.; and Yang, Y. 2020 · 2020
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{RNNL}ogic: Learning Logic Rules for Reasoning on Knowledge Graphs
Qu, M.; Chen, J.; Xhonneux, L.-P.; Bengio, Y.; and Tang, J. 2021 · 2021
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