Demba Ba. Submitted. “Deeply-sparse signal representations.” IEEE Transactions on Signal Processing. arXiv Version PDF Version
Abiy Tasissa, Emmanouil Theodosis, Bahareh Tolooshams, and Demba Ba. Submitted. “Dense and Sparse Coding: Theory and Architectures”. arXiv Version
Bahareh Tolooshams*, Andrew H. Song*, Simona Temereanca, and Demba Ba. 2020. “Convolutional dictionary learning based auto-encoders for natural exponential-family distributions.” in Proc. International Conference on Machine Learning (ICML). arXiv Version
Andrew H. Song*, Bahareh Tolooshams*, Simona Temereanca, and Demba Ba. 2020. “Convolutional Dictionary Learning of Stimulus from Spiking Data.” In Computational and Systems Neuroscience (COSYNE). PDF Version
Andrew H. Song, Francisco J. Flores, and Demba Ba. 2020. “Convolutional Dictionary Learning with Grid Refinement.” IEEE Transactions on Signal Processing. PDF Version
Bahareh Tolooshams, Sourav Dey, and Demba Ba. 2020. “Deep Residual Auto-Encoders for Expectation Maximization-inpired Dictionary Learning.” IEEE Transactions on Neural Networks and Learning Systems. arXiv Version
Andrew H. Song, Leon Chlon, Hugo Soulat, John Tauber, Sandya Subramanian, Demba Ba, and Michael J. Prerau. 4/2019. “Multitaper Infinite Hidden Markov Model for EEG.” In International Engineering in Medicine and Biology Conference (EMBC) 4/2019. Abstract

Electroencephalographam (EEG) monitoring of neural activity is widely used for identifying underlying brain states. For inference of brain states, researchers have often used Hidden Markov Models (HMM) with a fixed number of hidden states and an observation model linking the temporal dynamics embedded in EEG to the hidden states. The use of fixed states may be limiting, in that 1) pre-defined states might not capture the heterogeneous neural dynamics across individuals and 2) the oscillatory dynamics of the neural activity are not directly modeled. To this end, we use a Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM), which discovers the set of hidden states that best describes the EEG data, without a-priori specification of state number. In addition, we introduce an observation model based on classical asymptotic results of frequency domain properties of stationary time series, along with the description of the conditional distributions for Gibbs sampler inference. We then combine this with multitaper spectral estimation to reduce the variance of the spectral estimates. By applying our method to simulated data inspired by sleep EEG, we arrive at two main results: 1) the algorithm faithfully recovers the spectral characteristics of the true states, as well as the right number of states and 2) the incorporation of the multitaper framework produces a more stable estimate than traditional periodogram spectral estimates.

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Alexander Lin, Yingzhou Zhang, Jeremy Heng, Stephen A. Allsop, Kay M. Tye, Pierre E. Jacob, and Demba Ba. 2019. “Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach.” in Proc. International Conference on Artificial Intelligence and Statistics (AISTATS) 2019.Abstract

We propose a general statistical framework for clustering multiple time series that exhibit nonlinear dynamics into an a-priori unknown number of sub-groups. Our motivation comes from neuroscience, where an important problem is to identify, within a large assembly of neurons, subsets that respond similarly to a stimulus or contingency. Upon modeling the multiple time series as the output of a Dirichlet process mixture of nonlinear state-space models, we derive a Metropolis-within-Gibbs algorithm for full Bayesian inference that alternates between sampling cluster assignments and sampling parameter values that form the basis of the clustering. The Metropolis step employs recent innovations in particle-based methods. We apply the framework to clustering time series acquired from the prefrontal cortex of mice in an experiment designed to characterize the neural underpinnings of fear.

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Javier Zazo, Bahareh Tolooshams, and Demba Ba. 2019. “Convolutional Dictionary Learning in Hierarchical Networks.” in Proc. IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). arXiv VersionAbstract
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 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. This recursion describes a linear dynamic system that is a non-Gaussian Markov process across scales and is closely related to multilayer-convolutional sparse coding (ML-CSC) generative model for deep networks, except that our model allows for deeper architectures, and combines sparse and non-sparse signal representations. We propose an alternating minimization algorithm for learning the filters in this hierarchical model given observations at layer zero, e.g., natural images. The algorithm alternates between a coefficient-estimation step and a filter update step. The coefficient update step performs sparse (detail) and smooth (scale) coding and, when unfolded, leads to a deep neural network. We use MNIST to demonstrate the representation capabilities of the model, and its derived features (coefficients) for classification.
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Thomas Chang*, Bahareh Tolooshams*, and Demba Ba. 2019. “RandNet: deep learning with compressed measurements of images.” in Proc. IEEE 29th International Workshop on Machine Learning and Signal Processing (MLSP). Pittsburgh, PA. arXiv VersionAbstract
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 methods, the higher the dimensions of the input data, the longer it takes to learn. We introduce a class of neural networks, termed RandNet, for learning representations using compressed random measurements of data of interest, such as images. RandNet extends the convolutional recurrent sparse auto-encoder architecture to dense networks and, more importantly, to the case when the input data are compressed random measurements of the original data. Compressing the input data makes it possible to fit a larger number of batches in memory during training. Moreover, in the case of sparse measurements,training is more efficient computationally. We demonstrate that, in unsupervised settings, RandNet performs dictionary learning using compressed data. In supervised settings, we show that RandNet can classify MNIST images with minimal loss in accuracy, despite being trained with random projections of the images that result in a 50% reduction in size. Overall, our results provide a general principled framework for training neural networks using compressed data.
Yinghzuo Zhang, Noa Malem-Shinitski, Stephen A. Allsop, Kay Tye, and Demba Ba. 4/2018. “Estimating a separably-Markov random field (SMuRF) from binary observations.” Neural Computation, 30, 4, Pp. 1046-1079. Publisher's Version PDF Version
Stephen A Allsop, Romy Wichmann, Fergil Mills, Anthony Burgos-Robles, Chia-Jung Chang, Ada C. Felix-Ortiz, Alienor Vienne, Anna Beyeler, Ehsan M. Izadmehr, Gordon Glober, Meghan I. Cum, Johanna Stergiadou, Kavitha K. Anandalingham, Kathryn Farris, Praneeth Namburi, Christopher A. Leppla, Javier C. Weddington, Edward H. Nieh, Anne C. Smith, Demba Ba, Emery N. Brown, and Kay M. Tye. 3/3/2018. “Corticoamygdala transfer of socially derived information gates observational learning.” Cell, 173, 6, Pp. 1329-1342. Publisher's Version
Gabriel Schamberg, Demba Ba, and Todd P Coleman. 2/15/2018. “A modularized efficient framework for non-Markov time-series estimation.” IEEE Transactions on Signal Processing, 66, 12. Publisher's Version
Seong-Eun Kim, Michael Behr, Demba Ba, and Emery N. Brown. 1/2/2018. “State-space multitaper time-frequency analysis.” Proceedings of the National Academy of Sciences, 115, 1. Publisher's Version
Bahareh Tolooshams, Sourav Dey, and Demba Ba. 2018. “Scalable convolutional dictionary learning with constrained recurrent sparse auto-encoders.” in Proc. IEEE 28th International Worskhop on Machine Learning and Signal Processing (MLSP). arXiv Version
Noa Malem-Shiniski, Yingzhuo Zhang, Daniel T. Gray, Sarah N. Burke, Anne C. Smith, Carol A. Barnes, and Demba. Ba. 2018. “A separable two-dimensional random field model of binary response data from multi-day behavioral experiments.” Journal of Neursocience Methods, 307, Pp. 175-187. Publisher's Version
Noa Shinitski, Yingzhuo Zhang, Daniel T Gray, Sarah N Burke, Anne C Smith, Carol A Barnes, and Demba Ba. 2017. “Can you teach an old monkey a new trick?” Cosyne 2017. PDF Version
Yingzhuo Zhang, Noa Shinitski, Stephen Allsop, Kay Tye, and Demba Ba. 2017. “A Two-Dimensional Seperable Random Field Model of Within and Cross-Trial Neural Spiking Dynamics.” Cosyne 2017. zhang_cosyne_2017.pdf
Gabriel Schamberg, Demba Ba, Mark Wagner, and Todd Coleman. 2016. “Efficient low-rank spectrotemporal decomposition using ADMM.” In Statistical Signal Processing Workshop (SSP), 2016 IEEE, Pp. 1–5. IEEE.
Jonathan D Kenny, Jessica J Chemali, Joseph F Cotten, Christa J Van Dort, Seong-Eun Kim, Demba Ba, Norman E Taylor, Emery N Brown, and Ken Solt. 2016. “Physostigmine and Methylphenidate Induce Distinct Arousal States During Isoflurane General Anesthesia in Rats.” Anesthesia and analgesia.