Brain signals measured over a series of experiments have inherent variability because of
different physical and mental conditions among multiple subjects and sessions. Such …

Motivation The development of high-throughput single-cell sequencing technologies now
allows the investigation of the population diversity of cellular transcriptomes. The expression …

Non-negative matrix factorization (NMF) has become a popular method for learning
interpretable patterns from data. As one of the variants of standard NMF, convolutive NMF …

Several problems in neuroimaging and beyond require inference on the parameters of multi-
task sparse hierarchical regression models. Examples include M/EEG inverse problems …

This paper presents a new Bayesian nonnegative matrix factorization (NMF) for monaural
source separation. Using this approach, the reconstruction error based on NMF is …

Abstract Non-negative Matrix Factorization (NMF) is a traditional unsupervised machine
learning technique for decomposing a matrix into a set of bases and coefficients under the …

It proposes a deep inverse reinforcement learning method for slow and weak moving targets
detection in aerial video. Differential gray images of adjacent frames are used as the …

In this paper, we address the task of hyperspectral image classification using a 3-D
Convolutional Neural Network (CNN). Instead of commonly used raw spectral features …

Song recommendation from listening counts is now a classical problem, addressed by
different kinds of collaborative filtering (CF) techniques. Among them, Poisson matrix …

In this letter, we introduce a discriminative training algorithm of the basis vectors in the
nonnegative matrix factorization (NMF) model for single-channel speech enhancement. The …