暂无分享,去创建一个
Yoshua Bengio | Vijay Prakash Dwivedi | Thomas Laurent | Chaitanya K. Joshi | Xavier Bresson | Yoshua Bengio | T. Laurent | X. Bresson
[1] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[2] Andrew McCallum,et al. Automating the Construction of Internet Portals with Machine Learning , 2000, Information Retrieval.
[3] Vinayak A. Rao,et al. Relational Pooling for Graph Representations , 2019, ICML.
[4] Joan Bruna,et al. On the equivalence between graph isomorphism testing and function approximation with GNNs , 2019, NeurIPS.
[5] Max Welling,et al. Modeling Relational Data with Graph Convolutional Networks , 2017, ESWC.
[6] Martin Grohe,et al. Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks , 2018, AAAI.
[7] Samuel S. Schoenholz,et al. Neural Message Passing for Quantum Chemistry , 2017, ICML.
[8] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[9] Ulrike von Luxburg,et al. A tutorial on spectral clustering , 2007, Stat. Comput..
[10] Trevor Darrell,et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[11] Yizhou Sun,et al. Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification , 2019, ArXiv.
[12] Yoshua Bengio,et al. Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon , 2018, Eur. J. Oper. Res..
[13] Omer Levy,et al. SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems , 2019, NeurIPS.
[14] Jitendra Malik,et al. Spectral grouping using the Nystrom method , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[15] Shirley Ho,et al. Learning Symbolic Physics with Graph Networks , 2019, ArXiv.
[16] Philip S. Yu,et al. A Comprehensive Survey on Graph Neural Networks , 2019, IEEE Transactions on Neural Networks and Learning Systems.
[17] Rob Fergus,et al. Learning Multiagent Communication with Backpropagation , 2016, NIPS.
[18] Yuxiao Dong,et al. Microsoft Academic Graph: When experts are not enough , 2020, Quantitative Science Studies.
[19] Razvan Pascanu,et al. Relational inductive biases, deep learning, and graph networks , 2018, ArXiv.
[20] Davide Bacciu,et al. A Fair Comparison of Graph Neural Networks for Graph Classification , 2020, ICLR.
[21] Zhuwen Li,et al. Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search , 2018, NeurIPS.
[22] Emmanuel Abbe,et al. Community Detection and Stochastic Block Models , 2017, Found. Trends Commun. Inf. Theory.
[23] Xavier Bresson,et al. An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem , 2019, ArXiv.
[24] Le Song,et al. 2 Common Formulation for Greedy Algorithms on Graphs , 2018 .
[25] Robert D. Tortora,et al. Sampling: Design and Analysis , 2000 .
[26] Joan Bruna,et al. Spectral Networks and Locally Connected Networks on Graphs , 2013, ICLR.
[27] Pascal Fua,et al. SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[28] Yaron Lipman,et al. Invariant and Equivariant Graph Networks , 2018, ICLR.
[29] Pierre Vandergheynst,et al. Geometric Deep Learning: Going beyond Euclidean data , 2016, IEEE Signal Process. Mag..
[30] Xavier Bresson,et al. A Two-Step Graph Convolutional Decoder for Molecule Generation , 2019, ArXiv.
[31] Jeffrey Dean,et al. 1.1 The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design , 2019, 2020 IEEE International Solid- State Circuits Conference - (ISSCC).
[32] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[33] M. Bronstein,et al. Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning , 2019, Nature Methods.
[34] Jure Leskovec,et al. Redundancy-Free Computation Graphs for Graph Neural Networks , 2019, ArXiv.
[35] Marc Brockschmidt,et al. GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation , 2019, ICML.
[36] Christopher R'e,et al. Machine Learning on Graphs: A Model and Comprehensive Taxonomy , 2020, ArXiv.
[37] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[38] Emmanuel Abbe,et al. Community detection and stochastic block models: recent developments , 2017, Found. Trends Commun. Inf. Theory.
[39] Jure Leskovec,et al. Hierarchical Graph Representation Learning with Differentiable Pooling , 2018, NeurIPS.
[40] Yaron Lipman,et al. Provably Powerful Graph Networks , 2019, NeurIPS.
[41] Lihui Chen,et al. Capsule Graph Neural Network , 2018, ICLR.
[42] Mohamed R. Amer,et al. Understanding Attention and Generalization in Graph Neural Networks , 2019, NeurIPS.
[43] Maithra Raghu,et al. A Survey of Deep Learning for Scientific Discovery , 2020, ArXiv.
[44] Xavier Bresson,et al. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.
[45] Jan Eric Lenssen,et al. Fast Graph Representation Learning with PyTorch Geometric , 2019, ArXiv.
[46] Ah Chung Tsoi,et al. The Graph Neural Network Model , 2009, IEEE Transactions on Neural Networks.
[47] Yoshua Bengio,et al. Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.
[48] Hongzi Mao,et al. Learning scheduling algorithms for data processing clusters , 2018, SIGCOMM.
[49] Jonathan Masci,et al. Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[50] Yoshua Bengio,et al. Convolutional networks for images, speech, and time series , 1998 .
[51] Ryan G. Coleman,et al. ZINC: A Free Tool to Discover Chemistry for Biology , 2012, J. Chem. Inf. Model..
[52] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[53] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[54] Alán Aspuru-Guzik,et al. Convolutional Networks on Graphs for Learning Molecular Fingerprints , 2015, NIPS.
[55] Xavier Bresson,et al. Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks , 2017, NIPS.
[56] Boris Katz,et al. ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models , 2019, NeurIPS.
[57] Quoc V. Le,et al. Chip Placement with Deep Reinforcement Learning , 2020, ArXiv.
[58] Jure Leskovec,et al. Open Graph Benchmark: Datasets for Machine Learning on Graphs , 2020, NeurIPS.
[59] Jaewoo Kang,et al. Self-Attention Graph Pooling , 2019, ICML.
[60] Simon Haykin,et al. GradientBased Learning Applied to Document Recognition , 2001 .
[61] Jure Leskovec,et al. Graph Convolutional Neural Networks for Web-Scale Recommender Systems , 2018, KDD.
[62] Davide Bacciu,et al. A Gentle Introduction to Deep Learning for Graphs , 2019, Neural Networks.
[63] Xavier Bresson,et al. Residual Gated Graph ConvNets , 2017, ArXiv.
[64] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[65] Diego Marcheggiani,et al. Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling , 2017, EMNLP.
[66] Raia Hadsell,et al. Graph networks as learnable physics engines for inference and control , 2018, ICML.
[67] Davide Eynard,et al. Fake News Detection on Social Media using Geometric Deep Learning , 2019, ArXiv.
[68] Regina Barzilay,et al. Junction Tree Variational Autoencoder for Molecular Graph Generation , 2018, ICML.
[69] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[70] Yaron Lipman,et al. On the Universality of Invariant Networks , 2019, ICML.
[71] Jitendra Malik,et al. Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..
[72] Stephan Günnemann,et al. Pitfalls of Graph Neural Network Evaluation , 2018, ArXiv.
[73] Samy Bengio,et al. Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks , 2019, KDD.
[74] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[75] Jitendra Malik. Technical Perspective: What led computer vision to deep learning? , 2017, Commun. ACM.
[76] Jure Leskovec,et al. How Powerful are Graph Neural Networks? , 2018, ICLR.
[77] Davide Eynard,et al. SIGN: Scalable Inception Graph Neural Networks , 2020, ArXiv.
[78] Pietro Liò,et al. On Graph Classification Networks, Datasets and Baselines , 2019, ArXiv.
[79] Jure Leskovec,et al. Inductive Representation Learning on Large Graphs , 2017, NIPS.
[80] Razvan Pascanu,et al. Interaction Networks for Learning about Objects, Relations and Physics , 2016, NIPS.
[81] Navdeep Jaitly,et al. Pointer Networks , 2015, NIPS.
[82] Yvan Saeys,et al. Essential guidelines for computational method benchmarking , 2018, Genome Biology.
[83] Alexander J. Smola,et al. Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs , 2019, ArXiv.
[84] Takanori Maehara,et al. Revisiting Graph Neural Networks: All We Have is Low-Pass Filters , 2019, ArXiv.
[85] Jure Leskovec,et al. Learning to Simulate Complex Physics with Graph Networks , 2020, ICML.
[86] Bernard Ghanem,et al. DeepGCNs: Making GCNs Go as Deep as CNNs , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[87] Ken-ichi Kawarabayashi,et al. Representation Learning on Graphs with Jumping Knowledge Networks , 2018, ICML.
[88] Christopher R'e,et al. Low-Dimensional Hyperbolic Knowledge Graph Embeddings , 2020, ACL.
[89] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[90] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[91] Jure Leskovec,et al. Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation , 2018, NeurIPS.
[92] Rob Fergus,et al. Visualizing and Understanding Convolutional Networks , 2013, ECCV.
[93] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[94] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[95] Balasubramaniam Srinivasan,et al. On the Equivalence between Node Embeddings and Structural Graph Representations , 2019, ICLR 2020.
[96] Jure Leskovec,et al. Position-aware Graph Neural Networks , 2019, ICML.
[97] Max Welling,et al. Attention, Learn to Solve Routing Problems! , 2018, ICLR.
[98] Zhiyuan Liu,et al. Graph Neural Networks: A Review of Methods and Applications , 2018, AI Open.
[99] Mikhail Belkin,et al. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.