12 citations · 67 across the 40 of their papers we have counts for
7 papers · 1 filter
Sparse Multi-Family Deep Scattering Network
Romain Cosentino, Randall Balestriero
In this work, we propose the Sparse Multi-Family Deep Scattering Network (SMF-DSN), a novel architecture exploiting the interpretability of the Deep Scattering Network (DSN) and im…
Max-Affine Spline Insights into Deep Generative Networks
Randall Balestriero, Sebastien Paris, Richard Baraniuk
We connect a large class of Generative Deep Networks (GDNs) with spline operators in order to derive their properties, limitations, and new opportunities. By characterizing the lat…
Mad Max: Affine Spline Insights into Deep Learning
Randall Balestriero, Richard Baraniuk
We build a rigorous bridge between deep networks (DNs) and approximation theory via spline functions and operators. Our key result is that a large class of DNs can be written as a…
Semi-Supervised Learning via New Deep Network Inversion
Randall Balestriero, Vincent Roger, Herve G. Glotin +1
We exploit a recently derived inversion scheme for arbitrary deep neural networks to develop a new semi-supervised learning framework that applies to a wide range of systems and pr…
Deep Neural Networks
Randall Balestriero, Richard Baraniuk
Deep Neural Networks (DNNs) are universal function approximators providing state-of- the-art solutions on wide range of applications. Common perceptual tasks such as speech recogni…
Linear Time Complexity Deep Fourier Scattering Network and Extension to Nonlinear Invariants
Randall Balestriero, Herve Glotin
In this paper we propose a scalable version of a state-of-the-art deterministic time-invariant feature extraction approach based on consecutive changes of basis and nonlinearities,…
Robust Unsupervised Transient Detection With Invariant Representation based on the Scattering Network
Randall Balestriero, Behnaam Aazhang
We present a sparse and invariant representation with low asymptotic complexity for robust unsupervised transient and onset zone detection in noisy environments. This unsupervised…