Representing visual data using compact binary codes is attracting increa...
Class-incremental learning (CIL) learns a classification model with trai...
Pattern classification with compact representation is an important compo...
Training convolutional neural networks (CNNs) with back-propagation (BP)...
Objective: Heart rate variability (HRV) has been proven to be an importa...
Graph-based subspace clustering methods have exhibited promising perform...
Decoupled learning is a branch of model parallelism which parallelizes t...
Connectionist Temporal Classification (CTC) and attention mechanism are ...
Using the back-propagation (BP) to train neural networks requires a
sequ...
In this article, we show that solving the system of linear equations by
...
An extension of the regularized least-squares in which the estimation
pa...
Constrained adaptive filtering algorithms inculding constrained least me...