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[1] K. J. Craik,et al. The nature of explanation , 1944 .
[2] Jian-Yun Nie,et al. RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space , 2018, ICLR.
[3] D. Gentner,et al. Structure mapping in analogy and similarity. , 1997 .
[4] Alexander J. Smola,et al. Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning , 2017, ICLR.
[5] Razvan Pascanu,et al. Learning Deep Generative Models of Graphs , 2018, ICLR 2018.
[6] C. Koch,et al. Integrated information theory: from consciousness to its physical substrate , 2016, Nature Reviews Neuroscience.
[7] David L. Dill,et al. Learning a SAT Solver from Single-Bit Supervision , 2018, ICLR.
[8] Lorenzo Rosasco,et al. Holographic Embeddings of Knowledge Graphs , 2015, AAAI.
[9] Hoifung Poon,et al. Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text , 2016, ACL.
[10] Jianfeng Gao,et al. Embedding Entities and Relations for Learning and Inference in Knowledge Bases , 2014, ICLR.
[11] Daniel D. Johnson,et al. Learning Graphical State Transitions , 2016, ICLR.
[12] John Miller,et al. Traversing Knowledge Graphs in Vector Space , 2015, EMNLP.
[13] Lexing Xie,et al. Action Schema Networks: Generalised Policies with Deep Learning , 2017, AAAI.
[14] Tom M. Mitchell,et al. Incorporating Vector Space Similarity in Random Walk Inference over Knowledge Bases , 2014, EMNLP.
[15] Jason Weston,et al. Translating Embeddings for Modeling Multi-relational Data , 2013, NIPS.
[16] Zhiyuan Liu,et al. Learning Entity and Relation Embeddings for Knowledge Graph Completion , 2015, AAAI.
[17] Jure Leskovec,et al. GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models , 2018, ICML.
[18] Risi Kondor,et al. Covariant Compositional Networks For Learning Graphs , 2018, ICLR.
[19] Dai Quoc Nguyen,et al. A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network , 2017, NAACL.
[20] Razvan Pascanu,et al. Relational Deep Reinforcement Learning , 2018, ArXiv.
[21] Zhen Wang,et al. Knowledge Graph Embedding by Translating on Hyperplanes , 2014, AAAI.
[22] Marc Brockschmidt,et al. Learning to Represent Programs with Graphs , 2017, ICLR.
[23] Joan Bruna,et al. A Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks , 2017, ArXiv.
[24] Yuji Matsumoto,et al. Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach , 2017, ArXiv.
[25] Samy Bengio,et al. Neural Combinatorial Optimization with Reinforcement Learning , 2016, ICLR.
[26] Daniel Oñoro-Rubio,et al. Representation Learning for Visual-Relational Knowledge Graphs , 2017, ArXiv.
[27] Pasquale Minervini,et al. Convolutional 2D Knowledge Graph Embeddings , 2017, AAAI.
[28] Yoshua Bengio,et al. The Consciousness Prior , 2017, ArXiv.
[29] Rob Fergus,et al. Learning Multiagent Communication with Backpropagation , 2016, NIPS.
[30] Max Welling,et al. Attention Solves Your TSP , 2018, ArXiv.
[31] Xinlei Chen,et al. Iterative Visual Reasoning Beyond Convolutions , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[32] Nicolas Usunier,et al. Canonical Tensor Decomposition for Knowledge Base Completion , 2018, ICML.
[33] Razvan Pascanu,et al. Learning model-based planning from scratch , 2017, ArXiv.
[34] Ken-ichi Kawarabayashi,et al. Representation Learning on Graphs with Jumping Knowledge Networks , 2018, ICML.
[35] Rajarshi Das,et al. Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks , 2016, EACL.
[36] K. Holyoak,et al. A symbolic-connectionist theory of relational inference and generalization. , 2003, Psychological review.
[37] Razvan Pascanu,et al. Relational inductive biases, deep learning, and graph networks , 2018, ArXiv.
[38] Joshua B. Tenenbaum,et al. Building machines that learn and think like people , 2016, Behavioral and Brain Sciences.
[39] Danqi Chen,et al. Observed versus latent features for knowledge base and text inference , 2015, CVSC.
[40] Abhinav Gupta,et al. Non-local Neural Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[41] Razvan Pascanu,et al. Relational recurrent neural networks , 2018, NeurIPS.
[42] Ole Winther,et al. Recurrent Relational Networks , 2017, NeurIPS.
[43] Guillaume Bouchard,et al. Complex Embeddings for Simple Link Prediction , 2016, ICML.
[44] Wenhan Xiong,et al. DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning , 2017, EMNLP.
[45] M. Kerszberg,et al. A Neuronal Model of a Global Workspace in Effortful Cognitive Tasks , 2001 .
[46] Huanbo Luan,et al. Modeling Relation Paths for Representation Learning of Knowledge Bases , 2015, EMNLP.
[47] Razvan Pascanu,et al. A simple neural network module for relational reasoning , 2017, NIPS.
[48] Joan Bruna,et al. Deep Convolutional Networks on Graph-Structured Data , 2015, ArXiv.
[49] Josh Weisberg,et al. Higher-order theories of consciousness , 2008, Scholarpedia.
[50] Samuel S. Schoenholz,et al. Neural Message Passing for Quantum Chemistry , 2017, ICML.
[51] Fabian M. Suchanek,et al. YAGO3: A Knowledge Base from Multilingual Wikipedias , 2015, CIDR.
[52] Le Song,et al. 2 Common Formulation for Greedy Algorithms on Graphs , 2018 .
[53] Vijay S. Pande,et al. Molecular graph convolutions: moving beyond fingerprints , 2016, Journal of Computer-Aided Molecular Design.
[54] Razvan Pascanu,et al. Discovering objects and their relations from entangled scene representations , 2017, ICLR.
[55] Bowen Zhou,et al. A Structured Self-attentive Sentence Embedding , 2017, ICLR.
[56] Robert Van Gulick,et al. 4. Higher-order global states (HOGS): An alternative higher-order model of consciousness , 2004 .
[57] Stephan Günnemann,et al. NetGAN: Generating Graphs via Random Walks , 2018, ICML.
[58] Nicola De Cao,et al. MolGAN: An implicit generative model for small molecular graphs , 2018, ArXiv.
[59] Mathias Niepert,et al. Learning Convolutional Neural Networks for Graphs , 2016, ICML.
[60] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[61] Jun Zhao,et al. Knowledge Graph Embedding via Dynamic Mapping Matrix , 2015, ACL.
[62] John R. Anderson. Acquisition of cognitive skill. , 1982 .
[63] Joshua B. Tenenbaum,et al. A Compositional Object-Based Approach to Learning Physical Dynamics , 2016, ICLR.
[64] R. Zemel,et al. Neural Relational Inference for Interacting Systems , 2018, ICML.
[65] Razvan Pascanu,et al. Metacontrol for Adaptive Imagination-Based Optimization , 2017, ICLR.
[66] Pierre Vandergheynst,et al. Geometric Deep Learning: Going beyond Euclidean data , 2016, IEEE Signal Process. Mag..
[67] Sanja Fidler,et al. NerveNet: Learning Structured Policy with Graph Neural Networks , 2018, ICLR.
[68] Jessica B. Hamrick,et al. Relational inductive bias for physical construction in humans and machines , 2018, CogSci.
[69] Razvan Pascanu,et al. Interaction Networks for Learning about Objects, Relations and Physics , 2016, NIPS.
[70] Raia Hadsell,et al. Graph networks as learnable physics engines for inference and control , 2018, ICML.
[71] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[72] Chen Liang,et al. Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision , 2016, ACL.
[73] Razvan Pascanu,et al. Visual Interaction Networks: Learning a Physics Simulator from Video , 2017, NIPS.
[74] Tom M. Mitchell,et al. Random Walk Inference and Learning in A Large Scale Knowledge Base , 2011, EMNLP.
[75] Richard S. Zemel,et al. Gated Graph Sequence Neural Networks , 2015, ICLR.
[76] Xavier Bresson,et al. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.
[77] Yedid Hoshen,et al. VAIN: Attentional Multi-agent Predictive Modeling , 2017, NIPS.
[78] Alán Aspuru-Guzik,et al. Convolutional Networks on Graphs for Learning Molecular Fingerprints , 2015, NIPS.
[79] Joan Bruna,et al. Spectral Networks and Locally Connected Networks on Graphs , 2013, ICLR.
[80] Ah Chung Tsoi,et al. The Graph Neural Network Model , 2009, IEEE Transactions on Neural Networks.
[81] Fan Yang,et al. Differentiable Learning of Logical Rules for Knowledge Base Reasoning , 2017, NIPS.
[82] Yoshua Bengio,et al. Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.
[83] William W. Cohen. TensorLog: A Differentiable Deductive Database , 2016, ArXiv.
[84] Yelong Shen,et al. M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search , 2018, NeurIPS.