Symmetry-adapted representation learning

[1]  Ivor W. Tsang,et al.  Progressive Stochastic Learning for Noisy Labels , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[2]  Stefano Soatto,et al.  Emergence of Invariance and Disentanglement in Deep Representations , 2017, 2018 Information Theory and Applications Workshop (ITA).

[3]  Ivor W. Tsang,et al.  Robust Plackett–Luce model for k-ary crowdsourced preferences , 2018, Machine Learning.

[4]  Stefano Soatto,et al.  Emergence of invariance and disentangling in deep representations , 2017 .

[5]  Nanning Zheng,et al.  Constructing Deep Sparse Coding Network for image classification , 2017, Pattern Recognit..

[6]  Barnabás Póczos,et al.  Equivariance Through Parameter-Sharing , 2017, ICML.

[7]  Max Welling,et al.  Steerable CNNs , 2016, ICLR.

[8]  Michael Elad,et al.  Convolutional Neural Networks Analyzed via Convolutional Sparse Coding , 2016, J. Mach. Learn. Res..

[9]  Lorenzo Rosasco,et al.  Unsupervised learning of invariant representations , 2016, Theor. Comput. Sci..

[10]  Max Welling,et al.  Group Equivariant Convolutional Networks , 2016, ICML.

[11]  Koray Kavukcuoglu,et al.  Exploiting Cyclic Symmetry in Convolutional Neural Networks , 2016, ICML.

[12]  Babak Hassibi,et al.  Group Frames With Few Distinct Inner Products and Low Coherence , 2015, IEEE Transactions on Signal Processing.

[13]  Youfu Li,et al.  Integral invariants for space motion trajectory matching and recognition , 2015, Pattern Recognit..

[14]  Andrew Zisserman,et al.  Spatial Transformer Networks , 2015, NIPS.

[15]  Lorenzo Rosasco,et al.  On Invariance and Selectivity in Representation Learning , 2015, ArXiv.

[16]  Dacheng Tao,et al.  Deformed Graph Laplacian for Semisupervised Learning , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[17]  Andrea Vedaldi,et al.  Understanding Image Representations by Measuring Their Equivariance and Equivalence , 2014, International Journal of Computer Vision.

[18]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[19]  Lorenzo Rosasco,et al.  Discriminative template learning in group-convolutional networks for invariant speech representations , 2015, INTERSPEECH.

[20]  Andre Martins,et al.  Orbit Regularization , 2014, NIPS.

[21]  Pedro M. Domingos,et al.  Deep Symmetry Networks , 2014, NIPS.

[22]  Rolf P. Würtz,et al.  Learning invariant object recognition from temporal correlation in a hierarchical network , 2014, Neural Networks.

[23]  László Tóth,et al.  Combining time- and frequency-domain convolution in convolutional neural network-based phone recognition , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[24]  A. Fry Notes on the Paper “Cayley: On the theory of groups as depending on the symbolic equation θn = 1” , 2014 .

[25]  Stéphane Mallat,et al.  Rotation, Scaling and Deformation Invariant Scattering for Texture Discrimination , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Stéphane Mallat,et al.  Invariant Scattering Convolution Networks , 2012, IEEE transactions on pattern analysis and machine intelligence.

[28]  Peter G. Casazza,et al.  Finite Frames: Theory and Applications , 2012 .

[29]  Christopher K. I. Williams,et al.  Transformation Equivariant Boltzmann Machines , 2011, ICANN.

[30]  Ba Di Ya,et al.  Matrix Analysis , 2011 .

[31]  Bruno A. Olshausen,et al.  Lie Group Transformation Models for Predictive Video Coding , 2011, 2011 Data Compression Conference.

[32]  Stéphane Mallat,et al.  Group Invariant Scattering , 2011, ArXiv.

[33]  Quoc V. Le,et al.  Tiled convolutional neural networks , 2010, NIPS.

[34]  R. Fergus,et al.  Learning invariant features through topographic filter maps , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[35]  João M. F. Xavier,et al.  ANSIG—An analytic signature for permutation-invariant two-dimensional shape representation , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[36]  Rajesh P. N. Rao,et al.  Learning the Lie Groups of Visual Invariance , 2007, Neural Computation.

[37]  Hans Burkhardt,et al.  Invariant kernel functions for pattern analysis and machine learning , 2007, Machine Learning.

[38]  Thomas Serre,et al.  Robust Object Recognition with Cortex-Like Mechanisms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[39]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[40]  Bruno A. Olshausen,et al.  A multiscale dynamic routing circuit for forming size- and position-invariant object representations , 1995, Journal of Computational Neuroscience.

[41]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[42]  Yonina C. Eldar,et al.  Geometrically uniform frames , 2001, IEEE Trans. Inf. Theory.

[43]  Yonina C. Eldar Least-squares inner product shaping , 2002, Linear Algebra and its Applications.

[44]  Xiaoming Huo,et al.  Uncertainty principles and ideal atomic decomposition , 2001, IEEE Trans. Inf. Theory.

[45]  Mahmoud I. Khalil,et al.  Invariant 2D object recognition using the wavelet modulus maxima , 2000, Pattern Recognit. Lett..

[46]  T. Poggio,et al.  Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.

[47]  Stephen J. Wright,et al.  Numerical Optimization , 2018, Fundamental Statistical Inference.

[48]  Rajesh P. N. Rao,et al.  Learning Lie Groups for Invariant Visual Perception , 1998, NIPS.

[49]  JEFFREY WOOD,et al.  Invariant pattern recognition: A review , 1996, Pattern Recognit..

[50]  Gerald Sommer,et al.  A Lie group approach to steerable filters , 1995, Pattern Recognit. Lett..

[51]  Luc Van Gool,et al.  Vision and Lie's approach to invariance , 1995, Image Vis. Comput..

[52]  Jan Flusser,et al.  Pattern recognition by affine moment invariants , 1993, Pattern Recognit..

[53]  Michael Spann,et al.  A comparison between Fourier-Mellin descriptors and moment based features for invariant object recognition using neural networks , 1991, Pattern Recognit. Lett..

[54]  P. Fldik,et al.  Learning Invariance from Transformation Sequences , 1991, Neural Computation.

[55]  Yaser S. Abu-Mostafa,et al.  Learning from hints in neural networks , 1990, J. Complex..

[56]  Reiner Lenz,et al.  Group invariant pattern recognition , 1990, Pattern Recognit..

[57]  W S McCulloch,et al.  A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.

[58]  Zhi-Qiang Liu,et al.  On the minimum number of templates required for shift, rotation and size invariant pattern recognition , 1988, Pattern Recognit..

[59]  Geoffrey E. Hinton,et al.  Learning symmetry groups with hidden units: beyond the perceptron , 1986 .

[60]  D. Slepian Group codes for the Gaussian channel , 1968 .

[61]  Arthur Cayley The Collected Mathematical Papers: On the Theory of Groups as depending on the Symbolical Equation θn = 1 , 1854 .