Demba Ba

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Harvard University

Manifold Learning and Deep Clustering with Local Dictionaries

We introduce a novel clustering algorithm for data sampled from a union ...
12/03/2020 ∙ by Pranay Tankala, et al. ∙ 0 ∙ share

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PLSO: A generative framework for decomposing nonstationary timeseries into piecewise stationary oscillatory components

To capture the slowly time-varying spectral content of real-world time s...
10/22/2020 ∙ by Andrew H. Song, et al. ∙ 0 ∙ share

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Unfolding Neural Networks for Compressive Multichannel Blind Deconvolution

We propose a learned-structured unfolding neural network for the problem...
10/22/2020 ∙ by Bahareh Tolooshams, et al. ∙ 2 ∙ share

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Dense and Sparse Coding: Theory and Architectures

The sparse representation model has been successfully utilized in a numb...
06/16/2020 ∙ by Abiy Tasissa, et al. ∙ 0 ∙ share

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RandNet: deep learning with compressed measurements of images

Principal component analysis, dictionary learning, and auto-encoders are...
08/25/2019 ∙ by Thomas Chang, et al. ∙ 0 ∙ share

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Convolutional Dictionary Learning in Hierarchical Networks

Filter banks are a popular tool for the analysis of piecewise smooth sig...
07/23/2019 ∙ by Javier Zazo, et al. ∙ 0 ∙ share

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Fast Convolutional Dictionary Learning off the Grid

Given a continuous-time signal that can be modeled as the superposition ...
07/22/2019 ∙ by Andrew H. Song, et al. ∙ 0 ∙ share

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Deep Exponential-Family Auto-Encoders

We consider the problem of learning recurring convolutional patterns fro...
07/07/2019 ∙ by Bahareh Tolooshams, et al. ∙ 0 ∙ share

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Deep Residual Auto-Encoders for Expectation Maximization-based Dictionary Learning

Convolutional dictionary learning (CDL) has become a popular method for ...
04/18/2019 ∙ by Bahareh Tolooshams, et al. ∙ 0 ∙ share

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Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach

We propose a statistical framework for clustering multiple time series t...
10/23/2018 ∙ by Alexander Lin, et al. ∙ 0 ∙ share

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Sequential Detection of Regime Changes in Neural Data

The problem of detecting changes in firing patterns in neural data is st...
09/02/2018 ∙ by Taposh Banerjee, et al. ∙ 0 ∙ share

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Scalable Convolutional Dictionary Learning with Constrained Recurrent Sparse Auto-encoders

Given a convolutional dictionary underlying a set of observed signals, c...
07/12/2018 ∙ by Bahareh Tolooshams, et al. ∙ 0 ∙ share

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Deeply-Sparse Signal rePresentations (DS^2P)

The solution to the regularized least-squares problem min_x∈R^p+1/2y-Ax_...
07/05/2018 ∙ by Demba Ba, et al. ∙ 0 ∙ share

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Spike Sorting by Convolutional Dictionary Learning

Spike sorting refers to the problem of assigning action potentials obser...
06/06/2018 ∙ by Andrew H. Song, et al. ∙ 0 ∙ share

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Multitaper Spectral Estimation HDP-HMMs for EEG Sleep Inference

Electroencephalographic (EEG) monitoring of neural activity is widely us...
05/18/2018 ∙ by Leon Chlon, et al. ∙ 0 ∙ share

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Wavelet Shrinkage and Thresholding based Robust Classification for Brain Computer Interface

A macaque monkey is trained to perform two different kinds of tasks, mem...
10/27/2017 ∙ by Taposh Banerjee, et al. ∙ 0 ∙ share

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Estimating a Separably-Markov Random Field (SMuRF) from Binary Observations

A fundamental problem in neuroscience is to characterize the dynamics of...
09/27/2017 ∙ by Yingzhuo Zhang, et al. ∙ 0 ∙ share

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A Modularized Efficient Framework for Non-Markov Time Series Estimation

We present a compartmentalized approach to finding the maximum a-posteri...
06/14/2017 ∙ by Gabriel Schamberg, et al. ∙ 0 ∙ share

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Demba Ba

Featured Co-authors

Yonina C. Eldar

Vahid Tarokh

Todd P. Coleman

James M. Murphy

Taposh Banerjee

Bahareh Tolooshams

Jeremy Heng

Pierre Jacob

Abiy Tasissa

Andrew H. Song

Gabriel Schamberg

Manifold Learning and Deep Clustering with Local Dictionaries

PLSO: A generative framework for decomposing nonstationary timeseries into piecewise stationary oscillatory components

Unfolding Neural Networks for Compressive Multichannel Blind Deconvolution

Dense and Sparse Coding: Theory and Architectures

RandNet: deep learning with compressed measurements of images

Convolutional Dictionary Learning in Hierarchical Networks

Fast Convolutional Dictionary Learning off the Grid

Deep Exponential-Family Auto-Encoders

Deep Residual Auto-Encoders for Expectation Maximization-based Dictionary Learning

Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach

Sequential Detection of Regime Changes in Neural Data

Scalable Convolutional Dictionary Learning with Constrained Recurrent Sparse Auto-encoders

Deeply-Sparse Signal rePresentations (DS^2P)

Spike Sorting by Convolutional Dictionary Learning

Multitaper Spectral Estimation HDP-HMMs for EEG Sleep Inference

Wavelet Shrinkage and Thresholding based Robust Classification for Brain Computer Interface

Estimating a Separably-Markov Random Field (SMuRF) from Binary Observations

A Modularized Efficient Framework for Non-Markov Time Series Estimation