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Found 27 papers, 9 papers with code
Clustering Inductive Biases with Unrolled Networks
We propose an autoencoder architecture (WLSC) whose latent representations are implicitly, locally organized for spectral clustering through a Laplacian quadratic form of a bipartite graph, which generates a diverse set of artificial receptive fields that match primate data in V1 as faithfully as recent contrastive frameworks like Local Low Dimensionality, or LLD \citep{lld} that discard sparse dictionary learning.
An Efficient Algorithm for Clustered Multi-Task Compressive Sensing
This paper considers clustered multi-task compressive sensing, a hierarchical model that solves multiple compressive sensing tasks by finding clusters of tasks that leverage shared information to mutually improve signal reconstruction.
Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models
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.
Learning Linear Groups in Neural Networks
Employing equivariance in neural networks leads to greater parameter efficiency and improved generalization performance through the encoding of domain knowledge in the architecture; however, the majority of existing approaches require an a priori specification of the desired symmetries.
Sparse, Geometric Autoencoder Models of V1
The classical sparse coding model represents visual stimuli as a linear combination of a handful of learned basis functions that are Gabor-like when trained on natural image data.
Learning unfolded networks with a cyclic group structure
Deep neural networks lack straightforward ways to incorporate domain knowledge and are notoriously considered black boxes.
Unrolled Compressed Blind-Deconvolution
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.
High-Dimensional Sparse Bayesian Learning without Covariance Matrices
Sparse Bayesian learning (SBL) is a powerful framework for tackling the sparse coding problem.
Mixture Model Auto-Encoders: Deep Clustering through Dictionary Learning
State-of-the-art approaches for clustering high-dimensional data utilize deep auto-encoder architectures.
Stable and Interpretable Unrolled Dictionary Learning
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.
Covariance-Free Sparse Bayesian Learning
The most popular inference algorithms for SBL exhibit prohibitively large computational costs for high-dimensional problems due to the need to maintain a large covariance matrix.
Weighed $\ell_1$ on the simplex: Compressive sensing meets locality
Sparse manifold learning algorithms combine techniques in manifold learning and sparse optimization to learn features that could be utilized for downstream tasks.
Gaussian Process Convolutional Dictionary Learning
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.
On the convergence of group-sparse autoencoders
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.
K-Deep Simplex: Deep Manifold Learning via Local Dictionaries
We theoretically analyze the proposed program by relating the weighted $\ell_1$ penalty in KDS to a weighted $\ell_0$ program.
Unfolding Neural Networks for Compressive Multichannel Blind Deconvolution
We propose a learned-structured unfolding neural network for the problem of compressive sparse multichannel blind-deconvolution.
Towards improving discriminative reconstruction via simultaneous dense and sparse coding
We propose a novel dense and sparse coding model that integrates both representation capability and discriminative features.
RandNet: deep learning with compressed measurements of images
We introduce a class of neural networks, termed RandNet, for learning representations using compressed random measurements of data of interest, such as images.
Convolutional Dictionary Learning in Hierarchical Networks
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.
Fast Convolutional Dictionary Learning off the Grid
Given a continuous-time signal that can be modeled as the superposition of localized, time-shifted events from multiple sources, the goal of Convolutional Dictionary Learning (CDL) is to identify the location of the events--by Convolutional Sparse Coding (CSC)--and learn the template for each source--by Convolutional Dictionary Update (CDU).
Convolutional dictionary learning based auto-encoders for natural exponential-family distributions
We introduce a class of auto-encoder neural networks tailored to data from the natural exponential family (e. g., count data).
Deep Residual Autoencoders for Expectation Maximization-inspired Dictionary Learning
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).
Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach
We propose a general statistical framework for clustering multiple time series that exhibit nonlinear dynamics into an a-priori-unknown number of sub-groups.
Scalable Convolutional Dictionary Learning with Constrained Recurrent Sparse Auto-encoders
We demonstrate the ability of CRsAE to recover the underlying dictionary and characterize its sensitivity as a function of SNR.
Deeply-Sparse Signal rePresentations ($\text{D}\text{S}^2\text{P}$)
A recent line of work shows that a deep neural network with ReLU nonlinearities arises from a finite sequence of cascaded sparse coding models, the outputs of which, except for the last element in the cascade, are sparse and unobservable.
Multitaper Spectral Estimation HDP-HMMs for EEG Sleep Inference
Electroencephalographic (EEG) monitoring of neural activity is widely used for sleep disorder diagnostics and research.
Exact and Stable Recovery of Sequences of Signals with Sparse Increments via Differential _1-Minimization
We consider the problem of recovering a sequence of vectors, $(x_k)_{k=0}^K$, for which the increments $x_k-x_{k-1}$ are $S_k$-sparse (with $S_k$ typically smaller than $S_1$), based on linear measurements $(y_k = A_k x_k + e_k)_{k=1}^K$, where $A_k$ and $e_k$ denote the measurement matrix and noise, respectively.
