暂无分享,去创建一个
Jun Zhu | Yinpeng Dong | Zhijie Deng | Jun Zhu | Zhijie Deng | Yinpeng Dong
[1] Bo Zhang,et al. Smooth Neighbors on Teacher Graphs for Semi-Supervised Learning , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[2] Le Song,et al. Stochastic Training of Graph Convolutional Networks with Variance Reduction , 2017, ICML.
[3] Xiao-Ming Wu,et al. Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning , 2018, AAAI.
[4] 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).
[5] Sida I. Wang,et al. Dropout Training as Adaptive Regularization , 2013, NIPS.
[6] Tsuyoshi Murata,et al. {m , 1934, ACML.
[7] Harri Valpola,et al. Weight-averaged consistency targets improve semi-supervised deep learning results , 2017, ArXiv.
[8] Jun Zhu,et al. Stochastic Training of Graph Convolutional Networks , 2017, ICML 2018.
[9] Lise Getoor,et al. Collective Classification in Network Data , 2008, AI Mag..
[10] H. Goluba,et al. Eigenvalue computation in the 20 th century Gene , 2000 .
[11] Ruslan Salakhutdinov,et al. Revisiting Semi-Supervised Learning with Graph Embeddings , 2016, ICML.
[12] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[13] John B. Shoven,et al. I , Edinburgh Medical and Surgical Journal.
[14] Yoshua Bengio,et al. Semi-supervised Learning by Entropy Minimization , 2004, CAP.
[15] Steven Skiena,et al. DeepWalk: online learning of social representations , 2014, KDD.
[16] P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .
[17] Timo Aila,et al. Temporal Ensembling for Semi-Supervised Learning , 2016, ICLR.
[18] Xavier Bresson,et al. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.
[19] Renjie Liao,et al. Graph Partition Neural Networks for Semi-Supervised Classification , 2018, ICLR.
[20] Tolga Tasdizen,et al. Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning , 2016, NIPS.
[21] Jure Leskovec,et al. node2vec: Scalable Feature Learning for Networks , 2016, KDD.
[22] G. G. Stokes. "J." , 1890, The New Yale Book of Quotations.
[23] Zoubin Ghahramani,et al. Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.
[24] Yvan Saeys,et al. Lower bounds on the robustness to adversarial perturbations , 2017, NIPS.
[25] Jure Leskovec,et al. Inductive Representation Learning on Large Graphs , 2017, NIPS.
[26] Hossein Mobahi,et al. Deep Learning via Semi-supervised Embedding , 2012, Neural Networks: Tricks of the Trade.
[27] Shin Ishii,et al. Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[28] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[29] Andrew M. Dai,et al. Adversarial Training Methods for Semi-Supervised Text Classification , 2016, ICLR.
[30] David A. Wagner,et al. Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[31] Kannan Ramchandran,et al. Wavelet-regularized graph semi-supervised learning , 2013, 2013 IEEE Global Conference on Signal and Information Processing.
[32] Alexander Zien,et al. Semi-Supervised Classification by Low Density Separation , 2005, AISTATS.
[33] Stephan Günnemann,et al. Adversarial Attacks on Neural Networks for Graph Data , 2018, KDD.
[34] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[35] Mikhail Belkin,et al. Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..
[36] Le Song,et al. Adversarial Attack on Graph Structured Data , 2018, ICML.
[37] Mingzhe Wang,et al. LINE: Large-scale Information Network Embedding , 2015, WWW.