暂无分享,去创建一个
[1] Trevor Hastie,et al. Statistical Learning with Sparsity: The Lasso and Generalizations , 2015 .
[2] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[3] A. Laub,et al. A Schur-Fréchet Algorithm for Computing the Logarithm and Exponential of a Matrix , 1998, SIAM J. Matrix Anal. Appl..
[4] Dirk A. Lorenz. Book Review: Ulf Grenander and Michael Miller, Pattern Theory. From representation to inference. , 2007 .
[5] Xiaowei Zhou,et al. 3D Shape Reconstruction from 2D Landmarks: A Convex Formulation , 2014, ArXiv.
[6] Vittorio Murino,et al. Algorithmic Advances in Riemannian Geometry and Applications: For Machine Learning, Computer Vision, Statistics, and Optimization , 2016 .
[7] Subhransu Maji,et al. Visualizing and Understanding Deep Texture Representations , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[8] P. Bickel,et al. Regularized estimation of large covariance matrices , 2008, 0803.1909.
[9] Silvere Bonnabel,et al. Stochastic Gradient Descent on Riemannian Manifolds , 2011, IEEE Transactions on Automatic Control.
[10] Michael I. Miller,et al. Pattern Theory: From Representation to Inference , 2007 .
[11] Ruslan Salakhutdinov,et al. Scaling up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix , 2015, ICML.
[12] Franklin T. Luk,et al. A Rotation Method for Computing the QR-Decomposition , 1986 .
[13] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[14] Cristian Sminchisescu,et al. Matrix Backpropagation for Deep Networks with Structured Layers , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[15] C. Loan,et al. Nineteen Dubious Ways to Compute the Exponential of a Matrix , 1978 .
[16] Ales Leonardis,et al. Compositional Hierarchical Representation of Shape Manifolds for Classification of Non-manifold Shapes , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[17] Rama Chellappa,et al. Statistical analysis on Stiefel and Grassmann manifolds with applications in computer vision , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[18] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[19] Rama Chellappa,et al. Statistical Computations on Grassmann and Stiefel Manifolds for Image and Video-Based Recognition , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[20] N. Higham. The Scaling and Squaring Method for the Matrix Exponential Revisited , 2005, SIAM J. Matrix Anal. Appl..
[21] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[22] Anuj Srivastava,et al. Shape Analysis of Elastic Curves in Euclidean Spaces , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[23] Cleve B. Moler,et al. Nineteen Dubious Ways to Compute the Exponential of a Matrix, Twenty-Five Years Later , 1978, SIAM Rev..
[24] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[25] Razvan Pascanu,et al. Natural Neural Networks , 2015, NIPS.
[26] Roberto Cipolla,et al. Symmetry-invariant optimization in deep networks , 2015, ArXiv.
[27] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[28] Bamdev Mishra,et al. Manopt, a matlab toolbox for optimization on manifolds , 2013, J. Mach. Learn. Res..
[29] Marc Pollefeys,et al. The generalized trace-norm and its application to structure-from-motion problems , 2011, 2011 International Conference on Computer Vision.
[30] Abhishek Bhattacharya,et al. Nonparametric Inference on Manifolds: With Applications to Shape Spaces , 2015 .
[31] Ruslan Salakhutdinov,et al. Data-Dependent Path Normalization in Neural Networks , 2015, ICLR.
[32] Qiang Chen,et al. Network In Network , 2013, ICLR.
[33] Huan Liu. Feature Selection , 2010, Encyclopedia of Machine Learning.
[34] Geoffrey E. Hinton,et al. On the importance of initialization and momentum in deep learning , 2013, ICML.
[35] T. Bengtsson,et al. Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter variants , 2007 .
[36] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[37] Alan V. Oppenheim,et al. Trading accuracy for numerical stability: Orthogonalization, biorthogonalization and regularization , 2015, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[38] Yoshua Bengio,et al. Unitary Evolution Recurrent Neural Networks , 2015, ICML.
[39] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[40] Camille Couprie,et al. Learning Hierarchical Features for Scene Labeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[41] H. V. Trees,et al. Covariance, Subspace, and Intrinsic CramrRao Bounds , 2007 .
[42] Sayan Mukherjee,et al. The Information Geometry of Mirror Descent , 2013, IEEE Transactions on Information Theory.
[43] Jiri Matas,et al. All you need is a good init , 2015, ICLR.
[44] Klaus-Robert Müller,et al. Efficient BackProp , 2012, Neural Networks: Tricks of the Trade.
[45] Bernhard Schölkopf,et al. Multivariate Regression via Stiefel Manifold Constraints , 2004, DAGM-Symposium.
[46] Rémi Emonet,et al. Metric Learning as Convex Combinations of Local Models with Generalization Guarantees , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[47] Tim Salimans,et al. Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks , 2016, NIPS.
[48] Venu Govindaraju,et al. Normalization Propagation: A Parametric Technique for Removing Internal Covariate Shift in Deep Networks , 2016, ICML.
[49] Pierre Priouret,et al. Adaptive Algorithms and Stochastic Approximations , 1990, Applications of Mathematics.
[50] John M. Lee. Introduction to Smooth Manifolds , 2002 .
[51] Thomas Brox,et al. Striving for Simplicity: The All Convolutional Net , 2014, ICLR.
[52] Razvan Pascanu,et al. On the difficulty of training recurrent neural networks , 2012, ICML.
[53] Trevor Darrell,et al. Data-dependent Initializations of Convolutional Neural Networks , 2015, ICLR.
[54] J. Kiefer,et al. Stochastic Estimation of the Maximum of a Regression Function , 1952 .
[55] Surya Ganguli,et al. Exact solutions to the nonlinear dynamics of learning in deep linear neural networks , 2013, ICLR.
[56] Levent Tunçel,et al. Optimization algorithms on matrix manifolds , 2009, Math. Comput..
[57] Stefan Roth,et al. Discriminative shape from shading in uncalibrated illumination , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[58] Yui Man Lui,et al. Advances in matrix manifolds for computer vision , 2012, Image Vis. Comput..
[59] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[60] Bingbing Ni,et al. HCP: A Flexible CNN Framework for Multi-Label Image Classification , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[61] Matthijs Douze,et al. Large-scale image classification with trace-norm regularization , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[62] Timothy Doster,et al. Gradual DropIn of Layers to Train Very Deep Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[63] Bamdev Mishra,et al. Riemannian Preconditioning , 2014, SIAM J. Optim..
[64] Trevor Darrell,et al. Learning the Structure of Deep Convolutional Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[65] Tiejun Tong,et al. Shrinkage‐based Diagonal Discriminant Analysis and Its Applications in High‐Dimensional Data , 2009, Biometrics.
[66] Francis R. Bach,et al. Trace Lasso: a trace norm regularization for correlated designs , 2011, NIPS.
[67] D. Luenberger. Optimization by Vector Space Methods , 1968 .
[68] T. Mattfeldt. Stochastic Geometry and Its Applications , 1996 .
[69] Pierre-Antoine Absil,et al. Joint Diagonalization on the Oblique Manifold for Independent Component Analysis , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.
[70] Kilian Q. Weinberger,et al. Deep Networks with Stochastic Depth , 2016, ECCV.
[71] David M. Allen,et al. The Relationship Between Variable Selection and Data Agumentation and a Method for Prediction , 1974 .
[72] A. Ng. Feature selection, L1 vs. L2 regularization, and rotational invariance , 2004, Twenty-first international conference on Machine learning - ICML '04.
[73] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[74] Anuj Srivastava,et al. Riemannian Computing in Computer Vision , 2015 .
[75] Wotao Yin,et al. A feasible method for optimization with orthogonality constraints , 2013, Math. Program..
[76] Gene H. Golub,et al. Matrix computations , 1983 .