1 citations · 1 across the 5 of their papers we have counts for
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Learning unfolded networks with a cyclic group structure
Emmanouil Theodosis, Demba Ba
Deep neural networks lack straightforward ways to incorporate domain knowledge and are notoriously considered black boxes. Prior works attempted to inject domain knowledge into arc…
RandNet: deep learning with compressed measurements of images
Thomas Chang, Bahareh Tolooshams, Demba Ba
Principal component analysis, dictionary learning, and auto-encoders are all unsupervised methods for learning representations from a large amount of training data. In all these me…
Convolutional Dictionary Learning in Hierarchical Networks
Javier Zazo, Bahareh Tolooshams, Demba Ba
Filter banks are a popular tool for the analysis of piecewise smooth signals such as natural images. Motivated by the empirically observed properties of scale and detail coefficien…
Convolutional dictionary learning based auto-encoders for natural exponential-family distributions
Bahareh Tolooshams, Andrew H. Song, Simona Temereanca +1
We introduce a class of auto-encoder neural networks tailored to data from the natural exponential family (e.g., count data). The architectures are inspired by the problem of learn…
Deep Residual Autoencoders for Expectation Maximization-inspired Dictionary Learning
Bahareh Tolooshams, Sourav Dey, Demba Ba
We introduce a neural-network architecture, termed the constrained recurrent sparse autoencoder (CRsAE), that solves convolutional dictionary learning problems, thus establishing a…
Scalable Convolutional Dictionary Learning with Constrained Recurrent Sparse Auto-encoders
Bahareh Tolooshams, Sourav Dey, Demba Ba
Given a convolutional dictionary underlying a set of observed signals, can a carefully designed auto-encoder recover the dictionary in the presence of noise? We introduce an auto-e…