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06/05/2023
Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models
Latent Gaussian models have a rich history in statistics and machine lea...
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02/23/2023
SHAPER: Can You Hear the Shape of a Jet?
The identification of interesting substructures within jets is an import...
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02/22/2023
Sparse, Geometric Autoencoder Models of V1
The classical sparse coding model represents visual stimuli as a linear ...
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11/16/2022
Learning unfolded networks with a cyclic group structure
Deep neural networks lack straightforward ways to incorporate domain kno...
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09/28/2022
Learning Filter-Based Compressed Blind-Deconvolution
The problem of sparse multichannel blind deconvolution (S-MBD) arises fr...
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02/25/2022
High-Dimensional Sparse Bayesian Learning without Covariance Matrices
Sparse Bayesian learning (SBL) is a powerful framework for tackling the ...
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10/10/2021
Mixture Model Auto-Encoders: Deep Clustering through Dictionary Learning
State-of-the-art approaches for clustering high-dimensional data utilize...
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05/31/2021
PUDLE: Implicit Acceleration of Dictionary Learning by Backpropagation
The dictionary learning problem, representing data as a combination of f...
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05/21/2021
Covariance-Free Sparse Bayesian Learning
Sparse Bayesian learning (SBL) is a powerful framework for tackling the ...
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04/28/2021
Weighed ℓ_1 on the simplex: Compressive sensing meets locality
Sparse manifold learning algorithms combine techniques in manifold learn...
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03/28/2021
Gaussian Process Convolutional Dictionary Learning
Convolutional dictionary learning (CDL), the problem of estimating shift...
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02/13/2021
On the convergence of group-sparse autoencoders
Recent approaches in the theoretical analysis of model-based deep learni...
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12/03/2020
Manifold Learning and Deep Clustering with Local Dictionaries
We introduce a novel clustering algorithm for data sampled from a union ...
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10/22/2020
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...
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10/22/2020
Unfolding Neural Networks for Compressive Multichannel Blind Deconvolution
We propose a learned-structured unfolding neural network for the problem...
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06/16/2020
Dense and Sparse Coding: Theory and Architectures
The sparse representation model has been successfully utilized in a numb...
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08/25/2019
RandNet: deep learning with compressed measurements of images
Principal component analysis, dictionary learning, and auto-encoders are...
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07/23/2019
Convolutional Dictionary Learning in Hierarchical Networks
Filter banks are a popular tool for the analysis of piecewise smooth sig...
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07/22/2019
Fast Convolutional Dictionary Learning off the Grid
Given a continuous-time signal that can be modeled as the superposition ...
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07/07/2019
Deep Exponential-Family Auto-Encoders
We consider the problem of learning recurring convolutional patterns fro...
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04/18/2019
Deep Residual Auto-Encoders for Expectation Maximization-based Dictionary Learning
Convolutional dictionary learning (CDL) has become a popular method for ...
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10/23/2018
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...
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09/02/2018
Sequential Detection of Regime Changes in Neural Data
The problem of detecting changes in firing patterns in neural data is st...
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07/12/2018
Scalable Convolutional Dictionary Learning with Constrained Recurrent Sparse Auto-encoders
Given a convolutional dictionary underlying a set of observed signals, c...
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07/05/2018
Deeply-Sparse Signal rePresentations (DS^2P)
The solution to the regularized least-squares problem min_x∈R^p+1/2y-Ax_...
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06/06/2018
Spike Sorting by Convolutional Dictionary Learning
Spike sorting refers to the problem of assigning action potentials obser...
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05/18/2018
Multitaper Spectral Estimation HDP-HMMs for EEG Sleep Inference
Electroencephalographic (EEG) monitoring of neural activity is widely us...
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10/27/2017
Wavelet Shrinkage and Thresholding based Robust Classification for Brain Computer Interface
A macaque monkey is trained to perform two different kinds of tasks, mem...
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09/27/2017
Estimating a Separably-Markov Random Field (SMuRF) from Binary Observations
A fundamental problem in neuroscience is to characterize the dynamics of...
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06/14/2017
