GitHub

System object definitions for sparsity-aware image and volumetric data restoration

properties. For some features, we have prepared custom layer classes with Deep Learning Toolbox. It is now easy to incorporate them into flexible configurations and parts of your network. Ruiki ...
Scientific Research Publishing

Leaning Sparse Nonlinear Dynamical Systems via Frank-Wolfe Algorithm with Integral Strategy ()

Sparse identification of nonlinear dynamical systems is an important project, directly addressing the physics community’s long-standing goal of data-driven discovery. Although many effective methods ...
IEEE

Fault Line Selection and Section Location in Distribution Network Based on Sparse Representation

Abstract: Distribution network lines are widely distributed with high failure probability. Accurate identification of fault lines and fault areas can shorten the troubleshooting time. Based on the ...
eLife

Mapping odorant sensitivities reveals a sparse but structured representation of olfactory chemical space by sensory input to the mouse olfactory bulb

In olfactory systems, convergence of sensory neurons onto glomeruli generates a map of odorant receptor identity. How glomerular maps relate to sensory space remains unclear. We sought to better ...
C&EN

High-Resolution Hydrogen–Deuterium Protection Factors from Sparse Mass Spectrometry Data Validated by Nuclear Magnetic Resonance Measurements

School of Molecular and Cellular Biology, University of Leeds, LS2 9JT Leeds, United Kingdom ...
Frontiers

Co-sparse Non-negative Matrix Factorization

Non-negative matrix factorization, which decomposes the input non-negative matrix into product of two non-negative matrices, has been widely used in the neuroimaging field due to its flexible ...
Frontiers

A Sparsity-Driven Backpropagation-Less Learning Framework Using Populations of Spiking Growth Transform Neurons

Growth-transform (GT) neurons and their population models allow for independent control over the spiking statistics and the transient population dynamics while optimizing a physically plausible ...
PNAS

Inference of dynamic systems from noisy and sparse data via manifold-constrained Gaussian processes

Ordinary differential equations are a ubiquitous tool for modeling behaviors in science, such as gene regulation, biological rhythms, epidemics, and ecology. An important problem is to infer and ...
Europe PMC

RobOMP: Robust variants of Orthogonal Matching Pursuit for sparse representations.

Sparse coding aims to find a parsimonious representation of an example given an observation matrix or dictionary. In this regard, Orthogonal Matching Pursuit (OMP) provides an intuitive, simple and ...
IEEE

Wavelet-Integrated Alternating Sparse Dictionary Matrix Decomposition in Thermal Imaging CFRP Defect Detection

Abstract: With the increasing importance of using carbon fiber reinforced polymer (CFRP) composite in the aircraft industry, it becomes ever more critical to monitor the quality and health of CFRP ...