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Spectral Networks and Locally Connected Networks on Graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam +1
Convolutional Neural Networks are extremely efficient architectures in image and audio recognition tasks, thanks to their ability to exploit the local translational invariance of s…
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever +4
Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the…
Audio Texture Synthesis with Scattering Moments
Joan Bruna, Stéphane Mallat
We introduce an audio texture synthesis algorithm based on scattering moments. A scattering transform is computed by iteratively decomposing a signal with complex wavelet filter ba…
Intermittent process analysis with scattering moments
Joan Bruna, Stéphane Mallat, Emmanuel Bacry +1
Scattering moments provide nonparametric models of random processes with stationary increments. They are expected values of random variables computed with a nonexpansive operator,…
Blind Deconvolution with Non-local Sparsity Reweighting
Dilip Krishnan, Joan Bruna, Rob Fergus
Blind deconvolution has made significant progress in the past decade. Most successful algorithms are classified either as Variational or Maximum a-Posteriori (). In spite of t…
Signal Recovery from Pooling Representations
Joan Bruna, Arthur Szlam, Yann LeCun
In this work we compute lower Lipschitz bounds of pooling operators for as well as pooling operators preceded by half-rectification layers. These…