- Neural-Symbolic Learning and Reasoning: A Survey and Interpretation. Besold et al. arXiv:1711.03902
- Symbolic Logic meets Machine Learning: A Brief Survey in Infinite Domains. Belle, Vaishak et al. arXiv:2006.08480
- Neural Networks and Logical Reasoning Systems: a Translation Table. IJNS 2001
- Logic Mining Using Neural Networks. Sathasivam & Abdullah. ICIS 2005
- Markov logic networks. Richardson, Matthew & Domingos, Pedro. Machine Learning, 2006.
- Harnessing deep neural networks with logic rules. Hu, Zhiting. ACL 2016 [code]
- Logic tensor networks: Deep learning and logical reasoning from data and knowledge. Serafini, Luciano & Garcez, Artur D.Avila. arXiv:1606.04422 [pytorch] [tensorflow]
- Learning explanatory rules from noisy data. Evans, Richard & Grefenstette, Edward. IJCAI 2017 [code]
- Neural Arithmetic Logic Units. Trask, Andrew et al. NIPS 2018 [code1] [code2] [code3]
- A Semantic Loss Function for Deep Learning with Symbolic Knowledge. Xu, Jingyi et al. ICML 2018 [code]
- Learn to Explain Efficiently via Neural Logic Inductive Learning. Yang, Yuan & Song, Le. arXiv:1910.02481
- Neural Markov Logic Networks. Marra, Giuseppe & Kuželka, Ondřej. NIPS 2019
- Neural Logic Machines. Dong, Honghua et al. ICLR 2019 [code] [project]
- Neural Logic Reinforcement Learning. Jiang, Zhengyao & Luo, Shan. ICML 2019 [code]
- Neural Logic Rule Layers. Reimann, Jan Niclas & Schwung, Andreas. arXiv:1907.00878
- Neural Logic Networks. Shi, Shaoyun et al. arXiv:1910.08629 [project]
- Logic-inspired Deep Neural Networks. Le, Minh. arXiv:1911.08635
- A Novel Neural Network Structure Constructed according to Logical Relations. Wang, Gang. arXiv:1903.02683
- Augmenting Neural Networks with First-order Logic. Li, Tao & Srikumar, Vivek. ACL 2019 [code]
- A Logic-Driven Framework for Consistency of Neural Models. Li, Tao rt al. arXiv:1909.00126 [code]
- Semantic Interpretation of Deep Neural Networks Based on Continuous Logic. Dombi, József et al. arXiv:1910.02486
- Inductive Logic Programming via Differentiable Deep Neural Logic Networks. Payani, Ali & Fekri, Faramarz. ICLR 2020 [code]
- Transparent Classification with Multilayer Logical Perceptrons and Random Binarization. Wei Zhang et al. AAAI 2020
- iNALU: Improved Neural Arithmetic Logic Unit. Schlör, Daniel et al. arXiv:2003.07629
- The Logical Expressiveness of Graph Neural Networks. Finkbeiner, Bernd et al. arXiv:2003.04218
- Making Logic Learnable With Neural Networks. Brudermueller et al. arXiv:2002.03847
- Relational reasoning and generalization using non-symbolic neural networks.
- On sparse connectivity, adversarial robustness, and a novel model of the artificial neuron. arXiv:2006.09510
- Universally Quantized Neural Compression. arXiv:2006.09952
- Conversational Neuro-Symbolic Commonsense Reasoning. arXiv:2006.10022
- Logical Neural Networks. Riegel, Ryan et al. NeurIPS 2020
- Integrating Logical Rules Into Neural Multi-Hop Reasoning for Drug Repurposing. Liu, Yushan et al. ICML 2020
- Neural Logic Reasoning. Shi, Shaoyun et al. CIKM 2020
- Embedding Symbolic Knowledge into Deep Networks. Xie, Yaqi et al. NIPS 2019 [code]
- Probabilistic Logic Neural Networks for Reasoning. Qu, Meng & Tang, Jian. arXiv:1906.08495
- The Logical Expressiveness of Graph Neural Networks. ICLR 2020
- Efficient Probabilistic Logic Reasoning with Graph Neural Networks. Zhang, Yuyu et al. arXiv:2001.11850v2
- ICML20 workshop: pdf , homepage
- ProbLog: A Probabilistic Prolog and Its Applications to Link. De Raedt, Luc et al. IJCAI 2007 [project] [code]
- Deepproblog: Neural probabilistic logic programming. Manhaeve, Robin et al. NIPS 2018 [code]
- DL2: Training and Querying Neural Networks with Logic. Fischer, Marc et al. ICML 2019 [code]
- Interpretation and identification of causal mediation. Judea Pearl, 2014.
- (book) The Book of Why. Judea Pearl, 2018. [onedrive]
- (book) The Book of Why(中文版). Judea Pearl & Dana Mackenzie, 江⽣ & 于华 译, 2018. [onedrive]
- (book) Causality: Models, Reasoning, and Inference(2nd Edition). Judea Pearl, 2009. [onedrive]
- (book) Causal inference in statistics: An overview. Judea Pearl, on Statistics Surveys, 2009. [onedrive]
- (book) 因果推断简介. 丁鹏(北京大学). [onedrive]
- (book) Causal Inference - What If. Miguel A. Hernán & James M. Robins, 2019. [onedrive]
- (book) Elements of Causal Inference: Foundations and Learning Algorithms. MIT, 2020. [onedrive]
- (book) Introduction to Causal Inference: from a Machine Learning Perspective. Brady Neal, Course Lecture Notes, 2020. [onedrive]
- KDD 2020 Tutorial - Causal Inference and Stable Learning. [ppt]
- MLSS 2020 - Causility. [onedrive]
- MLSS 2020 - Causal Inference II. [onedrive]
- Visual Commonsense R-CNN. Wang, Tan et al. CVPR 2020 [code]
- Deconfounded image captioning: A causal retrospect. Yang, Xu et al. arXiv:2003.03923
- Two causal principles for improving visual dialog. Qi, Jiaxin et al. CVPR 2020
- Introduction to Judea Pearl's Do-Calculus. Robert R. Tucci. arXiv:1305.5506
- Causal induction from visual observations for goal directed tasks. Nair, Suraj et al. arXiv:1910.01751 [code]
- Unbiased scene graph generation from bi-ased training. Tang, Kaihua et al. CVPR 2020 [code]
- Discovering causal signals in images. Lopez-Paz et al. CVPR 2017
- CausalGAN: Learning causal implicit generative models with adversarial training. Kocaoglu, et al. ICLR 2018 [code]
- SAM: Structural agnostic model, causal discovery and penalized adversarial learning. Kalainathan et al. arXiv:1803.04929
- Causal reasoning from meta-reinforcement learning. Dasgupta et al. arXiv:1901.08162
- A meta-transfer objective for learning to disentangle causal mechanisms. Bengio, Yoshua et al. arXiv:1901.10912
- Visual causal feature learning. Chalupka et al. arXiv:1412.2309
- Fast Real-time Counterfactual Explanations. Zhao, Yunxia et al. ICML 2020
- Scientific Discovery by Generating Counterfactuals using Image Translation. Narayanaswamy et al. MICCAI 2020
- Structural Agnostic Modeling: Adversarial Learning of Causal Graphs. Kalainathan et al. arXiv:1803.04929
- Causal Discovery in Physical Systems from Videos. Li, Yunzhu et al. arXiv:2007.00631 [project]
- Causality Learning: A New Perspective for Interpretable Machine Learning. Xu, Guandong et al. arXiv:2006.16789
- Causal Modeling for Fairness in Dynamical Systems. Creager, Elliot et al. ICML 2020
- Causal Effect Identifiability under Partial-Observability Lee, Sanghack & Bareinboim, Elias. ICML 2020
- Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery. Tagasovska et al. ICML 2020
- Efficient Intervention Design for Causal Discovery with Latents. Addanki et al. ICML 2020
- Fast Real-time Counterfactual Explanations. Yunxia Zhao. ICML 2020
- Latent Instrumental Variables as Priors in Causal Inference based on Independence of Cause and Mechanism. Sokolovska et al. arXiv:2007.08812
- AiR: Attention with Reasoning Capability. Chen, Shi et al. ECCV 2020 [code]
- Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets. Kumor, Daniel et al. ICML 2020
- Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models. Saito, Yuta & Yasui, Shota. ICML 2020
- DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training. Kallus, Nathan. ICML 2020
- Causal Inference using Gaussian Processes with Structured Latent Confounders. Witty, Sam et al. ICML 2020
- Causal Effect Estimation and Optimal Dose Suggestions in Mobile Health. Zhu, Liangyu et al. ICML 2020
- Alleviating Privacy Attacks via Causal Learning. Tople, Shruti et al. ICML 2020
- SSCR: Iterative Language-Based Image Editing via Self-Supervised Counterfactual Reasoning. Fu, Tsu-Jui et al. EMNLP 2020
- Direct and Indirect Effects. Muller, Dominique & Judd, Charles M. Wiley StatsRef: Statistics Reference Online, 2003
- Causal Diagrams for Empirical Research. Pearl, Judea. American Statistician, 2011
- Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect. Tang, Kaihua et al. NeurIPS 2020 [code]
- Interventional Few-Shot Learning. Yue, Zhongqi et al. NeurIPS 2020 [code]
- Causal Intervention for Weakly-Supervised Semantic Segmentation. Zhang, Dong et al. NeurIPS 2020 [code]
- Deep Structural Causal Models for Tractable Counterfactual Inference. Pawlowski, Nick et al. NeurIPS 2020 [code]
- Causality for Machine Learning. Schölkopf, Bernhard. ICLR 2020
- Explaining the Efficacy of Counterfactually Augmented Data. iclr 2021
- Accounting for Unobserved Causalonfounding in Domain Generalization. iclr 2021
- Continual Lifelong Causal Effect Inference with Real-world Evidence. iclr 2021
- Counterfactual Generative Networks. iclr 2021
- Amortized Causal Discovery Learning to Infer Ccausal Graphs from Time Series Data. iclr 2021
- Selecting Treatment Effects Models for Domain Adaptation using Causal Knowledge. iclr 2021
- Disentangled Generative Causal Representation Learning. iclr 2021
- Multi-task Causal Learning with Gaussian Processes. Aglietti et al. NeurIPS 2020
- Causal Imitation Learning with Unobserved Confounders. Zhang, Junzhe et al. NeurIPS 2020
- Differentiable Causal Discovery from Interventional Data. Brouillard et al. NeurIPS 2020
- A Causal View on Robustness of Neural Networks. Zhang, Cheng et al. NeurIPS 2020
- Group invariance principles for causal generative models. Besserve et al. AISTATS 2018
- Causal Regularization. Bahadori et al. arXiv:1702.02604
Note: All papers pdf can be found and downloaded on Bing or Google.
Source: https://github.com/FLHonker/Awesome-Neural-Logic
Contact: Yuang Liu([email protected]), ECNU.