暂无分享,去创建一个
Yihao Fang | Lizhen Lin | Mohammad Rasool Izadi | Robert Stevenson | Lizhen Lin | R. Stevenson | M. Izadi | Yihao Fang
[1] Frederik Kunstner,et al. Limitations of the empirical Fisher approximation for natural gradient descent , 2019, NeurIPS.
[2] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[3] Xavier Bresson,et al. CayleyNets: Graph Convolutional Neural Networks With Complex Rational Spectral Filters , 2017, IEEE Transactions on Signal Processing.
[4] Lise Getoor,et al. Collective Classification in Network Data , 2008, AI Mag..
[5] Ruslan Salakhutdinov,et al. Revisiting Semi-Supervised Learning with Graph Embeddings , 2016, ICML.
[6] Ah Chung Tsoi,et al. The Graph Neural Network Model , 2009, IEEE Transactions on Neural Networks.
[7] Pascal Vincent,et al. Fast Approximate Natural Gradient Descent in a Kronecker-factored Eigenbasis , 2018, NeurIPS.
[8] Roger B. Grosse,et al. Optimizing Neural Networks with Kronecker-factored Approximate Curvature , 2015, ICML.
[9] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[11] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[12] Eric-Jan Wagenmakers,et al. A Tutorial on Fisher Information , 2017, 1705.01064.
[13] James Martens,et al. New perspectives on the natural gradient method , 2014, ArXiv.
[14] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[15] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[16] Pierre Vandergheynst,et al. Geometric Deep Learning: Going beyond Euclidean data , 2016, IEEE Signal Process. Mag..
[17] Philip S. Yu,et al. A Comprehensive Survey on Graph Neural Networks , 2019, IEEE Transactions on Neural Networks and Learning Systems.
[18] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[19] Alexander Kolesnikov,et al. Revisiting Self-Supervised Visual Representation Learning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Cao Xiao,et al. FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling , 2018, ICLR.
[21] James Martens,et al. New Insights and Perspectives on the Natural Gradient Method , 2014, J. Mach. Learn. Res..
[22] Zhiyuan Liu,et al. Graph Neural Networks: A Review of Methods and Applications , 2018, AI Open.
[23] Yoram Singer,et al. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..
[24] Shun-ichi Amari,et al. Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.
[25] Jan Eric Lenssen,et al. Fast Graph Representation Learning with PyTorch Geometric , 2019, ArXiv.