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Gaussian Process Convolutional Dictionary Learning
Convolutional dictionary learning (CDL), the problem of estimating shift...
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On the convergence of group-sparse autoencoders
Recent approaches in the theoretical analysis of model-based deep learni...
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Manifold Learning and Deep Clustering with Local Dictionaries
We introduce a novel clustering algorithm for data sampled from a union ...
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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...
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Unfolding Neural Networks for Compressive Multichannel Blind Deconvolution
We propose a learned-structured unfolding neural network for the problem...
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Dense and Sparse Coding: Theory and Architectures
The sparse representation model has been successfully utilized in a numb...
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RandNet: deep learning with compressed measurements of images
Principal component analysis, dictionary learning, and auto-encoders are...
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Convolutional Dictionary Learning in Hierarchical Networks
Filter banks are a popular tool for the analysis of piecewise smooth sig...
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Fast Convolutional Dictionary Learning off the Grid
Given a continuous-time signal that can be modeled as the superposition ...
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Deep Exponential-Family Auto-Encoders
We consider the problem of learning recurring convolutional patterns fro...
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Deep Residual Auto-Encoders for Expectation Maximization-based Dictionary Learning
Convolutional dictionary learning (CDL) has become a popular method for ...
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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...
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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...
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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...
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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_...
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Spike Sorting by Convolutional Dictionary Learning
Spike sorting refers to the problem of assigning action potentials obser...
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Multitaper Spectral Estimation HDP-HMMs for EEG Sleep Inference
Electroencephalographic (EEG) monitoring of neural activity is widely us...
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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...
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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...
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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...
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