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Theory

1,082 papers

Papers per year

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Papers

Axiomatic Attribution for Deep Networks ICML 2017
Optimal Architectures in a Solvable Model of Deep Networks NIPS 2016
Exponential expressivity in deep neural networks through transient chaos NIPS 2016
Globally Optimal Training of Generalized Polynomial Neural Networks with Nonlinear Spectral Methods NIPS 2016
Residual Networks Behave Like Ensembles of Relatively Shallow Networks NIPS 2016
Orthogonal Random Features NIPS 2016
Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity NIPS 2016
Deep Learning Games NIPS 2016
On the Quality of the Initial Basin in Overspecified Neural Networks ICML 2016
Convolutional Rectifier Networks as Generalized Tensor Decompositions ICML 2016
Discrete Deep Feature Extraction: A Theory and New Architectures ICML 2016
On the Role of Nonlinear Transformations in Deep Neural Network Acoustic Models INTERSPEECH 2016
Understanding Image Representations by Measuring Their Equivariance and Equivalence CVPR 2015
A Stable Multi-Scale Kernel for Topological Machine Learning CVPR 2015
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization NIPS 2014
On the Computational Efficiency of Training Neural Networks NIPS 2014
Altitude Training: Strong Bounds for Single-Layer Dropout NIPS 2014
On the Number of Linear Regions of Deep Neural Networks NIPS 2014
Signal recovery from Pooling Representations ICML 2014
Groupwise Registration via Graph Shrinkage on the Image Manifold CVPR 2013
On a Link Between Kernel Mean Maps and Fraunhofer Diffraction, with an Application to Super-Resolution Beyond the Diffraction Limit CVPR 2013
Guaranteed Sparse Recovery under Linear Transformation ICML 2013
Sharp Generalization Error Bounds for Randomly-projected Classifiers ICML 2013
Nyström Method vs Random Fourier Features: A Theoretical and Empirical Comparison NIPS 2012
A Note on Metric Properties for Some Divergence Measures: The Gaussian Case ACML 2012