暂无分享,去创建一个
Zhizhen Zhao | Renjie Liao | Raquel Urtasun | Richard S. Zemel | R. Zemel | R. Urtasun | Renjie Liao | Zhizhen Zhao
[1] William H. Press,et al. Numerical Recipes 3rd Edition: The Art of Scientific Computing , 2007 .
[2] Joan Bruna,et al. Community Detection with Graph Neural Networks , 2017 .
[3] Sanja Fidler,et al. 3D Graph Neural Networks for RGBD Semantic Segmentation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[4] Yoel Shkolnisky,et al. Diffusion Interpretation of Nonlocal Neighborhood Filters for Signal Denoising , 2009, SIAM J. Imaging Sci..
[5] Luca Antiga,et al. Automatic differentiation in PyTorch , 2017 .
[6] A. Singer. From graph to manifold Laplacian: The convergence rate , 2006 .
[7] Raquel Urtasun,et al. Deep Parametric Continuous Convolutional Neural Networks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[8] Pierre Vandergheynst,et al. Wavelets on Graphs via Spectral Graph Theory , 2009, ArXiv.
[9] Mathias Niepert,et al. Learning Graph Representations with Embedding Propagation , 2017, NIPS.
[10] Razvan Pascanu,et al. Learning Deep Generative Models of Graphs , 2018, ICLR 2018.
[11] Ruslan Salakhutdinov,et al. Revisiting Semi-Supervised Learning with Graph Embeddings , 2016, ICML.
[12] R. Coifman,et al. Non-linear independent component analysis with diffusion maps , 2008 .
[13] O. A. von Lilienfeld,et al. Electronic spectra from TDDFT and machine learning in chemical space. , 2015, The Journal of chemical physics.
[14] Pietro Liò,et al. Graph Attention Networks , 2017, ICLR.
[15] C. Lanczos. An iteration method for the solution of the eigenvalue problem of linear differential and integral operators , 1950 .
[16] Le Song,et al. Discriminative Embeddings of Latent Variable Models for Structured Data , 2016, ICML.
[17] Jure Leskovec,et al. Inductive Representation Learning on Large Graphs , 2017, NIPS.
[18] J. Tenenbaum,et al. A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.
[19] Mikhail Belkin,et al. Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..
[20] Mathias Niepert,et al. Learning Convolutional Neural Networks for Graphs , 2016, ICML.
[21] Amit Singer,et al. Detecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps , 2009, Proceedings of the National Academy of Sciences.
[22] Richard S. Zemel,et al. Gated Graph Sequence Neural Networks , 2015, ICLR.
[23] Shun-ichi Amari,et al. Methods of information geometry , 2000 .
[24] B. Parlett. The Symmetric Eigenvalue Problem , 1981 .
[25] Renjie Liao,et al. Graph Partition Neural Networks for Semi-Supervised Classification , 2018, ICLR.
[26] Razvan Pascanu,et al. Relational inductive biases, deep learning, and graph networks , 2018, ArXiv.
[27] Jordan B. Pollack,et al. Recursive Distributed Representations , 1990, Artif. Intell..
[28] R R Coifman,et al. Geometric diffusions as a tool for harmonic analysis and structure definition of data: multiscale methods. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[29] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.
[30] Xavier Bresson,et al. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.
[31] Mikhail Belkin,et al. Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.
[32] Ronen Talmon,et al. Empirical intrinsic geometry for nonlinear modeling and time series filtering , 2013, Proceedings of the National Academy of Sciences.
[33] Le Song,et al. Stochastic Training of Graph Convolutional Networks with Variance Reduction , 2017, ICML.
[34] Joan Bruna,et al. Deep Convolutional Networks on Graph-Structured Data , 2015, ArXiv.
[35] Samuel S. Schoenholz,et al. Neural Message Passing for Quantum Chemistry , 2017, ICML.
[36] Ronald R. Coifman,et al. Diffusion Maps, Spectral Clustering and Eigenfunctions of Fokker-Planck Operators , 2005, NIPS.
[37] P. Vandergheynst,et al. Accelerated filtering on graphs using Lanczos method , 2015, 1509.04537.
[38] Cao Xiao,et al. FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling , 2018, ICLR.
[39] Alán Aspuru-Guzik,et al. Convolutional Networks on Graphs for Learning Molecular Fingerprints , 2015, NIPS.
[40] Ulrike von Luxburg,et al. A tutorial on spectral clustering , 2007, Stat. Comput..
[41] Raia Hadsell,et al. Graph networks as learnable physics engines for inference and control , 2018, ICML.
[42] Donald F. Towsley,et al. Diffusion-Convolutional Neural Networks , 2015, NIPS.
[43] Regina Barzilay,et al. Junction Tree Variational Autoencoder for Molecular Graph Generation , 2018, ICML.
[44] Pierre Vandergheynst,et al. Graph Signal Processing: Overview, Challenges, and Applications , 2017, Proceedings of the IEEE.
[45] Kilian Q. Weinberger,et al. Metric Learning for Kernel Regression , 2007, AISTATS.
[46] Jure Leskovec,et al. node2vec: Scalable Feature Learning for Networks , 2016, KDD.
[47] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[48] Zhizhen Zhao,et al. Analog forecasting with dynamics-adapted kernels , 2014, 1412.3831.
[49] John D. Lafferty,et al. Diffusion Kernels on Statistical Manifolds , 2005, J. Mach. Learn. Res..
[50] Xiaojin Zhu,et al. Semi-Supervised Learning Literature Survey , 2005 .
[51] Dimitrios Giannakis,et al. Dynamics-Adapted Cone Kernels , 2014, SIAM J. Appl. Dyn. Syst..
[52] Pierre Vandergheynst,et al. Geometric Deep Learning: Going beyond Euclidean data , 2016, IEEE Signal Process. Mag..
[53] Hongyuan Zha,et al. Low-Rank Matrix Approximation Using the Lanczos Bidiagonalization Process with Applications , 1999, SIAM J. Sci. Comput..
[54] Sanja Fidler,et al. NerveNet: Learning Structured Policy with Graph Neural Networks , 2018, ICLR.
[55] D. Donoho,et al. Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[56] Pascal Frossard,et al. The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains , 2012, IEEE Signal Processing Magazine.
[57] Pietro Perona,et al. Self-Tuning Spectral Clustering , 2004, NIPS.
[58] Joseph Gomes,et al. MoleculeNet: a benchmark for molecular machine learning† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc02664a , 2017, Chemical science.
[59] Joan Bruna,et al. Few-Shot Learning with Graph Neural Networks , 2017, ICLR.
[60] Fan Chung,et al. Spectral Graph Theory , 1996 .
[61] Sanja Fidler,et al. Situation Recognition with Graph Neural Networks , 2018 .
[62] Andrew P. Witkin,et al. Scale-Space Filtering , 1983, IJCAI.
[63] Neta Rabin,et al. Multi-scale kernels for Nyström based extension schemes , 2018, Appl. Math. Comput..
[64] Joan Bruna,et al. Spectral Networks and Locally Connected Networks on Graphs , 2013, ICLR.
[65] Lisa Zhang,et al. Inference in Probabilistic Graphical Models by Graph Neural Networks , 2018, 2019 53rd Asilomar Conference on Signals, Systems, and Computers.
[66] Ah Chung Tsoi,et al. The Graph Neural Network Model , 2009, IEEE Transactions on Neural Networks.