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Found 17 papers, 2 papers with code
Data-Driven Low-Rank Neural Network Compression
Despite many modern applications of Deep Neural Networks (DNNs), the large number of parameters in the hidden layers makes them unattractive for deployment on devices with storage capacity constraints.
Efficacy of Bayesian Neural Networks in Active Learning
By performing a comprehensive set of experiments, we show that Bayesian neural networks are more efficient than ensemble based techniques in capturing uncertainty.
Matrix Completion in the Unit Hypercube via Structured Matrix Factorization
We tackle this challenge by using a two-fold approach: first, we transform this task into a constrained matrix completion problem with entries bounded in the unit interval [0, 1]; second, we propose two novel matrix factorization models that leverage our knowledge of the VFX environment.
LEARNING GENERATIVE MODELS FOR DEMIXING OF STRUCTURED SIGNALS FROM THEIR SUPERPOSITION USING GANS
Recently, Generative Adversarial Networks (GANs) have emerged as a popular alternative for modeling complex high dimensional distributions.
Minimum Uncertainty Based Detection of Adversaries in Deep Neural Networks
The effectiveness of the novel detectors in the context of competing alternatives is highlighted through extensive tests for various types of adversarial attacks with variable levels of strength.
Unsupervised Demixing of Structured Signals from Their Superposition Using GANs
In this paper, we consider the observation setting in which the samples from a target distribution are given by the superposition of two structured components, and leverage GANs for learning of the structure of the components.
Learning Generative Models of Structured Signals from Their Superposition Using GANs with Application to Denoising and Demixing
Recently, Generative Adversarial Networks (GANs) have emerged as a popular alternative for modeling complex high dimensional distributions.
Improved Support Recovery Guarantees for the Group Lasso With Applications to Structural Health Monitoring
This paper considers the problem of estimating an unknown high dimensional signal from noisy linear measurements, {when} the signal is assumed to possess a \emph{group-sparse} structure in a {known,} fixed dictionary.
Noisy Tensor Completion for Tensors with a Sparse Canonical Polyadic Factor
In this paper we study the problem of noisy tensor completion for tensors that admit a canonical polyadic or CANDECOMP/PARAFAC (CP) decomposition with one of the factors being sparse.
Block CUR: Decomposing Matrices using Groups of Columns
In this paper, we consider matrix approximation by sampling predefined \emph{blocks} of columns (or rows) from the matrix.
Rank-to-engage: New Listwise Approaches to Maximize Engagement
For many internet businesses, presenting a given list of items in an order that maximizes a certain metric of interest (e. g., click-through-rate, average engagement time etc.)
Noisy Inductive Matrix Completion Under Sparse Factor Models
This paper examines a general class of noisy matrix completion tasks where the underlying matrix is following an IMC model i. e., it is formed by a mixing matrix (a priori unknown) sandwiched between two known feature matrices.
A Compressed Sensing Based Decomposition of Electrodermal Activity Signals
The measurement and analysis of Electrodermal Activity (EDA) offers applications in diverse areas ranging from market research, to seizure detection, to human stress analysis.
On Convolutional Approximations to Linear Dimensionality Reduction Operators for Large Scale Data Processing
In this paper, we examine the problem of approximating a general linear dimensionality reduction (LDR) operator, represented as a matrix $A \in \mathbb{R}^{m \times n}$ with $m < n$, by a partial circulant matrix with rows related by circular shifts.
Noisy Matrix Completion under Sparse Factor Models
This paper examines a general class of noisy matrix completion tasks where the goal is to estimate a matrix from observations obtained at a subset of its entries, each of which is subject to random noise or corruption.
Backhaul-Constrained Multi-Cell Cooperation Leveraging Sparsity and Spectral Clustering
Dynamic clustered cooperation, where the sparse equalizer and the cooperation clusters are jointly determined, is solved via alternating minimization based on spectral clustering and group-sparse regression.
Compressive Measurement Designs for Estimating Structured Signals in Structured Clutter: A Bayesian Experimental Design Approach
This work considers an estimation task in compressive sensing, where the goal is to estimate an unknown signal from compressive measurements that are corrupted by additive pre-measurement noise (interference, or clutter) as well as post-measurement noise, in the specific setting where some (perhaps limited) prior knowledge on the signal, interference, and noise is available.
