4 citations · 10 across the 7 of their papers we have counts for
5 papers
Interpretable Image Clustering via Diffeomorphism-Aware K-Means
Romain Cosentino, Randall Balestriero, Yanis Bahroun +3
We design an interpretable clustering algorithm aware of the nonlinear structure of image manifolds. Our approach leverages the interpretability of -means applied in the image s…
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…
Nonlinear Regression with a Convolutional Encoder-Decoder for Remote Monitoring of Surface Electrocardiograms
Anton Banta, Romain Cosentino, Mathews M John +4
We propose the Nonlinear Regression Convolutional Encoder-Decoder (NRCED), a novel framework for mapping a multivariate input to a multivariate output. In particular, we implement…
The Geometry of Deep Networks: Power Diagram Subdivision
Randall Balestriero, Romain Cosentino, Behnaam Aazhang +1
We study the geometry of deep (neural) networks (DNs) with piecewise affine and convex nonlinearities. The layers of such DNs have been shown to be {\em max-affine spline operators…
Overcomplete Frame Thresholding for Acoustic Scene Analysis
Romain Cosentino, Randall Balestriero, Richard Baraniuk +1
In this work, we derive a generic overcomplete frame thresholding scheme based on risk minimization. Overcomplete frames being favored for analysis tasks such as classification, re…