no code implementations • 5 Jun 2023 • Alexander Lin, Bahareh Tolooshams, Yves Atchadé, Demba Ba
Latent Gaussian models have a rich history in statistics and machine learning, with applications ranging from factor analysis to compressed sensing to time series analysis.
no code implementations • 28 Sep 2022 • Bahareh Tolooshams, Satish Mulleti, Demba Ba, Yonina C. Eldar
To reduce its computational and implementation cost, we propose a compression method that enables blind recovery from much fewer measurements with respect to the full received signal in time.
no code implementations • 9 Dec 2021 • Bahareh Tolooshams, Kazuhito Koishida
Deep learning-based speech enhancement has shown unprecedented performance in recent years.
1 code implementation • 31 May 2021 • Bahareh Tolooshams, Demba Ba
The success of dictionary learning relies on access to a "good" initial estimate of the dictionary and the ability of the sparse coding step to provide an unbiased estimate of the code.
no code implementations • 28 Mar 2021 • Andrew H. Song, Bahareh Tolooshams, Demba Ba
Convolutional dictionary learning (CDL), the problem of estimating shift-invariant templates from data, is typically conducted in the absence of a prior/structure on the templates.
no code implementations • 13 Feb 2021 • Emmanouil Theodosis, Bahareh Tolooshams, Pranay Tankala, Abiy Tasissa, Demba Ba
Recent approaches in the theoretical analysis of model-based deep learning architectures have studied the convergence of gradient descent in shallow ReLU networks that arise from generative models whose hidden layers are sparse.
no code implementations • 22 Oct 2020 • Bahareh Tolooshams, Satish Mulleti, Demba Ba, Yonina C. Eldar
We propose a learned-structured unfolding neural network for the problem of compressive sparse multichannel blind-deconvolution.
no code implementations • 16 Jun 2020 • Abiy Tasissa, Emmanouil Theodosis, Bahareh Tolooshams, Demba Ba
We propose a novel dense and sparse coding model that integrates both representation capability and discriminative features.
1 code implementation • 30 Jan 2020 • Bahareh Tolooshams, Ritwik Giri, Andrew H. Song, Umut Isik, Arvindh Krishnaswamy
Supervised deep learning has gained significant attention for speech enhancement recently.
Ranked #2 on Speech Enhancement on CHiME-3
no code implementations • 25 Aug 2019 • Thomas Chang, Bahareh Tolooshams, Demba Ba
We introduce a class of neural networks, termed RandNet, for learning representations using compressed random measurements of data of interest, such as images.
no code implementations • 23 Jul 2019 • Javier Zazo, Bahareh Tolooshams, Demba Ba
Motivated by the empirically observed properties of scale and detail coefficients of images in the wavelet domain, we propose a hierarchical deep generative model of piecewise smooth signals that is a recursion across scales: the low pass scale coefficients at one layer are obtained by filtering the scale coefficients at the next layer, and adding a high pass detail innovation obtained by filtering a sparse vector.
1 code implementation • ICML 2020 • Bahareh Tolooshams, Andrew H. Song, Simona Temereanca, Demba Ba
We introduce a class of auto-encoder neural networks tailored to data from the natural exponential family (e. g., count data).
1 code implementation • 18 Apr 2019 • Bahareh Tolooshams, Sourav Dey, Demba Ba
Specifically, we leverage the interpretation of the alternating-minimization algorithm for dictionary learning as an approximate Expectation-Maximization algorithm to develop autoencoders that enable the simultaneous training of the dictionary and regularization parameter (ReLU bias).
1 code implementation • 12 Jul 2018 • Bahareh Tolooshams, Sourav Dey, Demba Ba
We demonstrate the ability of CRsAE to recover the underlying dictionary and characterize its sensitivity as a function of SNR.