暂无分享,去创建一个
Honglei Zhang | Hong Cheng | Jia Li | Kangfei Zhao | Tomasyu Yu Jiajin Li | YU Rong | Junzhou Huang | Hong Cheng | Jia Li | Yu Rong | Jiajin Li | Kangfei Zhao | Honglei Zhang | Jia Li | Y. Rong | Tomas Yu
[1] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[2] Xiao-Ming Wu,et al. Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning , 2018, AAAI.
[3] Andrew B. Kahng,et al. New spectral methods for ratio cut partitioning and clustering , 1991, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..
[4] Jure Leskovec,et al. node2vec: Scalable Feature Learning for Networks , 2016, KDD.
[5] Xavier Bresson,et al. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.
[6] Graham Neubig,et al. Lagging Inference Networks and Posterior Collapse in Variational Autoencoders , 2019, ICLR.
[7] Kilian Q. Weinberger,et al. Simplifying Graph Convolutional Networks , 2019, ICML.
[8] Eric T. Nalisnick,et al. Deep Generative Models with Stick-Breaking Priors , 2016 .
[9] Marco Cote. STICK-BREAKING VARIATIONAL AUTOENCODERS , 2017 .
[10] Ulrike von Luxburg,et al. A tutorial on spectral clustering , 2007, Stat. Comput..
[11] Michael I. Jordan,et al. Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..
[12] Thore Graepel,et al. Kernel Topic Models , 2011, AISTATS.
[13] Stefano Ermon,et al. Graphite: Iterative Generative Modeling of Graphs , 2018, ICML.
[14] Regina Barzilay,et al. Junction Tree Variational Autoencoder for Molecular Graph Generation , 2018, ICML.
[15] Jeffrey Mark Siskind,et al. Image Segmentation with Ratio Cut , 2003, IEEE Trans. Pattern Anal. Mach. Intell..
[16] Qi Liu,et al. Constrained Graph Variational Autoencoders for Molecule Design , 2018, NeurIPS.
[17] 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.
[18] Il-Chul Moon,et al. Dirichlet Variational Autoencoder , 2019, Pattern Recognit..
[19] Jure Leskovec,et al. Learning Structural Node Embeddings via Diffusion Wavelets , 2017, KDD.
[20] Jitendra Malik,et al. Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[21] Matthias Hein,et al. Spectral clustering based on the graph p-Laplacian , 2009, ICML '09.
[22] Max Welling,et al. Improved Variational Inference with Inverse Autoregressive Flow , 2016, NIPS 2016.
[23] Hao Zhang,et al. WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling , 2018, ICLR.
[24] Charles A. Sutton,et al. Autoencoding Variational Inference For Topic Models , 2017, ICLR.
[25] Pierre Vandergheynst,et al. Wavelets on Graphs via Spectral Graph Theory , 2009, ArXiv.
[26] Samuel S. Schoenholz,et al. Neural Message Passing for Quantum Chemistry , 2017, ICML.
[27] Dorothea Wagner,et al. Between Min Cut and Graph Bisection , 1993, MFCS.
[28] Christopher Burgess,et al. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework , 2016, ICLR 2016.
[29] Huawei Shen,et al. Graph Convolutional Networks using Heat Kernel for Semi-supervised Learning , 2019, IJCAI.
[30] Takanori Maehara,et al. Revisiting Graph Neural Networks: All We Have is Low-Pass Filters , 2019, ArXiv.
[31] Max Welling,et al. Variational Graph Auto-Encoders , 2016, ArXiv.
[32] William Yang Wang,et al. Dirichlet Variational Autoencoder for Text Modeling , 2018, ArXiv.
[33] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[34] Deli Zhao,et al. Network Representation Learning with Rich Text Information , 2015, IJCAI.
[35] Lise Getoor,et al. Collective Classification in Network Data , 2008, AI Mag..
[36] Sophie Burkhardt,et al. Decoupling Sparsity and Smoothness in the Dirichlet Variational Autoencoder Topic Model , 2019, J. Mach. Learn. Res..
[37] Stefano Ermon,et al. InfoVAE: Balancing Learning and Inference in Variational Autoencoders , 2019, AAAI.