FLHonker / Awesome-Neural-Logic

Awesome Neural Logic and Causality: MLN, NLRL, NLM, etc. 因果推断，神经逻辑，强人工智能逻辑推理前沿领域。

Awesome-Neural-Logic

Survey

1. Neural-Symbolic Learning and Reasoning: A Survey and Interpretation. Besold et al. arXiv:1711.03902
2. Symbolic Logic meets Machine Learning: A Brief Survey in Infinite Domains. Belle, Vaishak et al. arXiv:2006.08480

Neural Logic

1. Neural Networks and Logical Reasoning Systems: a Translation Table. IJNS 2001
2. Logic Mining Using Neural Networks. Sathasivam & Abdullah. ICIS 2005
3. Markov logic networks. Richardson, Matthew & Domingos, Pedro. Machine Learning, 2006.
4. Harnessing deep neural networks with logic rules. Hu, Zhiting. ACL 2016 [code]
5. Logic tensor networks: Deep learning and logical reasoning from data and knowledge. Serafini, Luciano & Garcez, Artur D.Avila. arXiv:1606.04422 [pytorch] [tensorflow]
6. Learning explanatory rules from noisy data. Evans, Richard & Grefenstette, Edward. IJCAI 2017 [code]
7. Neural Arithmetic Logic Units. Trask, Andrew et al. NIPS 2018 [code1] [code2] [code3]
8. A Semantic Loss Function for Deep Learning with Symbolic Knowledge. Xu, Jingyi et al. ICML 2018 [code]
9. Learn to Explain Efficiently via Neural Logic Inductive Learning. Yang, Yuan & Song, Le. arXiv:1910.02481
10. Neural Markov Logic Networks. Marra, Giuseppe & Kuželka, Ondřej. NIPS 2019
11. Neural Logic Machines. Dong, Honghua et al. ICLR 2019 [code] [project]
12. Neural Logic Reinforcement Learning. Jiang, Zhengyao & Luo, Shan. ICML 2019 [code]
13. Neural Logic Rule Layers. Reimann, Jan Niclas & Schwung, Andreas. arXiv:1907.00878
14. Neural Logic Networks. Shi, Shaoyun et al. arXiv:1910.08629 [project]
15. Logic-inspired Deep Neural Networks. Le, Minh. arXiv:1911.08635
16. A Novel Neural Network Structure Constructed according to Logical Relations. Wang, Gang. arXiv:1903.02683
17. Augmenting Neural Networks with First-order Logic. Li, Tao & Srikumar, Vivek. ACL 2019 [code]
18. A Logic-Driven Framework for Consistency of Neural Models. Li, Tao rt al. arXiv:1909.00126 [code]
19. Semantic Interpretation of Deep Neural Networks Based on Continuous Logic. Dombi, József et al. arXiv:1910.02486
20. Inductive Logic Programming via Differentiable Deep Neural Logic Networks. Payani, Ali & Fekri, Faramarz. ICLR 2020 [code]
21. Transparent Classification with Multilayer Logical Perceptrons and Random Binarization. Wei Zhang et al. AAAI 2020
22. iNALU: Improved Neural Arithmetic Logic Unit. Schlör, Daniel et al. arXiv:2003.07629
23. The Logical Expressiveness of Graph Neural Networks. Finkbeiner, Bernd et al. arXiv:2003.04218
24. Making Logic Learnable With Neural Networks. Brudermueller et al. arXiv:2002.03847
25. Relational reasoning and generalization using non-symbolic neural networks.
26. On sparse connectivity, adversarial robustness, and a novel model of the artificial neuron. arXiv:2006.09510
27. Universally Quantized Neural Compression. arXiv:2006.09952
28. Conversational Neuro-Symbolic Commonsense Reasoning. arXiv:2006.10022
29. Logical Neural Networks. Riegel, Ryan et al. NeurIPS 2020
30. Integrating Logical Rules Into Neural Multi-Hop Reasoning for Drug Repurposing. Liu, Yushan et al. ICML 2020
31. Neural Logic Reasoning. Shi, Shaoyun et al. CIKM 2020

Graph Neural Logic

1. Embedding Symbolic Knowledge into Deep Networks. Xie, Yaqi et al. NIPS 2019 [code]
2. Probabilistic Logic Neural Networks for Reasoning. Qu, Meng & Tang, Jian. arXiv:1906.08495
3. The Logical Expressiveness of Graph Neural Networks. ICLR 2020
4. Efficient Probabilistic Logic Reasoning with Graph Neural Networks. Zhang, Yuyu et al. arXiv:2001.11850v2
5. ICML20 workshop: pdf , homepage

Logic Programming

1. ProbLog: A Probabilistic Prolog and Its Applications to Link. De Raedt, Luc et al. IJCAI 2007 [project] [code]
2. Deepproblog: Neural probabilistic logic programming. Manhaeve, Robin et al. NIPS 2018 [code]
3. DL2: Training and Querying Neural Networks with Logic. Fischer, Marc et al. ICML 2019 [code]

Causality Books

1. Interpretation and identification of causal mediation. Judea Pearl, 2014.
2. (book) The Book of Why. Judea Pearl, 2018. [onedrive]
3. (book) The Book of Why(中文版). Judea Pearl & Dana Mackenzie, 江⽣ & 于华 译, 2018. [onedrive]
4. (book) Causality: Models, Reasoning, and Inference(2nd Edition). Judea Pearl, 2009. [onedrive]
5. (book) Causal inference in statistics: An overview. Judea Pearl, on Statistics Surveys, 2009. [onedrive]
6. (book) 因果推断简介. 丁鹏(北京大学). [onedrive]
7. (book) Causal Inference - What If. Miguel A. Hernán & James M. Robins, 2019. [onedrive]
8. (book) Elements of Causal Inference: Foundations and Learning Algorithms. MIT, 2020. [onedrive]
9. (book) Introduction to Causal Inference: from a Machine Learning Perspective. Brady Neal, Course Lecture Notes, 2020. [onedrive]

Causality PPT

1. KDD 2020 Tutorial - Causal Inference and Stable Learning. [ppt]
2. MLSS 2020 - Causility. [onedrive]
3. MLSS 2020 - Causal Inference II. [onedrive]

Causality papers

1. Visual Commonsense R-CNN. Wang, Tan et al. CVPR 2020 [code]
2. Deconfounded image captioning: A causal retrospect. Yang, Xu et al. arXiv:2003.03923
3. Two causal principles for improving visual dialog. Qi, Jiaxin et al. CVPR 2020
4. Introduction to Judea Pearl's Do-Calculus. Robert R. Tucci. arXiv:1305.5506
5. Causal induction from visual observations for goal directed tasks. Nair, Suraj et al. arXiv:1910.01751 [code]
6. Unbiased scene graph generation from bi-ased training. Tang, Kaihua et al. CVPR 2020 [code]
7. Discovering causal signals in images. Lopez-Paz et al. CVPR 2017
8. CausalGAN: Learning causal implicit generative models with adversarial training. Kocaoglu, et al. ICLR 2018 [code]
9. SAM: Structural agnostic model, causal discovery and penalized adversarial learning. Kalainathan et al. arXiv:1803.04929
10. Causal reasoning from meta-reinforcement learning. Dasgupta et al. arXiv:1901.08162
11. A meta-transfer objective for learning to disentangle causal mechanisms. Bengio, Yoshua et al. arXiv:1901.10912
12. Visual causal feature learning. Chalupka et al. arXiv:1412.2309
13. Fast Real-time Counterfactual Explanations. Zhao, Yunxia et al. ICML 2020
14. Scientific Discovery by Generating Counterfactuals using Image Translation. Narayanaswamy et al. MICCAI 2020
15. Structural Agnostic Modeling: Adversarial Learning of Causal Graphs. Kalainathan et al. arXiv:1803.04929
16. Causal Discovery in Physical Systems from Videos. Li, Yunzhu et al. arXiv:2007.00631 [project]
17. Causality Learning: A New Perspective for Interpretable Machine Learning. Xu, Guandong et al. arXiv:2006.16789
18. Causal Modeling for Fairness in Dynamical Systems. Creager, Elliot et al. ICML 2020
19. Causal Effect Identifiability under Partial-Observability Lee, Sanghack & Bareinboim, Elias. ICML 2020
20. Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery. Tagasovska et al. ICML 2020
21. Efficient Intervention Design for Causal Discovery with Latents. Addanki et al. ICML 2020
22. Fast Real-time Counterfactual Explanations. Yunxia Zhao. ICML 2020
23. Latent Instrumental Variables as Priors in Causal Inference based on Independence of Cause and Mechanism. Sokolovska et al. arXiv:2007.08812
24. AiR: Attention with Reasoning Capability. Chen, Shi et al. ECCV 2020 [code]
25. Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets. Kumor, Daniel et al. ICML 2020
26. Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models. Saito, Yuta & Yasui, Shota. ICML 2020
27. DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training. Kallus, Nathan. ICML 2020
28. Causal Inference using Gaussian Processes with Structured Latent Confounders. Witty, Sam et al. ICML 2020
29. Causal Effect Estimation and Optimal Dose Suggestions in Mobile Health. Zhu, Liangyu et al. ICML 2020
30. Alleviating Privacy Attacks via Causal Learning. Tople, Shruti et al. ICML 2020
31. SSCR: Iterative Language-Based Image Editing via Self-Supervised Counterfactual Reasoning. Fu, Tsu-Jui et al. EMNLP 2020
32. Direct and Indirect Effects. Muller, Dominique & Judd, Charles M. Wiley StatsRef: Statistics Reference Online, 2003
33. Causal Diagrams for Empirical Research. Pearl, Judea. American Statistician, 2011
34. Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect. Tang, Kaihua et al. NeurIPS 2020 [code]
35. Interventional Few-Shot Learning. Yue, Zhongqi et al. NeurIPS 2020 [code]
36. Causal Intervention for Weakly-Supervised Semantic Segmentation. Zhang, Dong et al. NeurIPS 2020 [code]
37. Deep Structural Causal Models for Tractable Counterfactual Inference. Pawlowski, Nick et al. NeurIPS 2020 [code]
38. Causality for Machine Learning. Schölkopf, Bernhard. ICLR 2020
39. Explaining the Efficacy of Counterfactually Augmented Data. iclr 2021
40. Accounting for Unobserved Causalonfounding in Domain Generalization. iclr 2021
41. Continual Lifelong Causal Effect Inference with Real-world Evidence. iclr 2021
42. Counterfactual Generative Networks. iclr 2021
43. Amortized Causal Discovery Learning to Infer Ccausal Graphs from Time Series Data. iclr 2021
44. Selecting Treatment Effects Models for Domain Adaptation using Causal Knowledge. iclr 2021
45. Disentangled Generative Causal Representation Learning. iclr 2021
46. Multi-task Causal Learning with Gaussian Processes. Aglietti et al. NeurIPS 2020
47. Causal Imitation Learning with Unobserved Confounders. Zhang, Junzhe et al. NeurIPS 2020
48. Differentiable Causal Discovery from Interventional Data. Brouillard et al. NeurIPS 2020
49. A Causal View on Robustness of Neural Networks. Zhang, Cheng et al. NeurIPS 2020
50. Group invariance principles for causal generative models. Besserve et al. AISTATS 2018
51. Causal Regularization. Bahadori et al. arXiv:1702.02604

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Note: All papers pdf can be found and downloaded on Bing or Google.

Source: https://github.com/FLHonker/Awesome-Neural-Logic

Contact: Yuang Liu([e-mail]), ECNU.