Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.

Neurocomputing welcomes theoretical contributions aimed at winning further understanding of neural networks and learning systems, including, but not restricted to, architectures, learning methods, analysis of network dynamics, theories of learning, self-organization, biological neural network modelling, sensorimotor transformations and interdisciplinary topics with artificial intelligence, artificial life, cognitive science, computational learning theory, fuzzy logic, genetic algorithms, information theory, machine learning, neurobiology and pattern recognition.

Neurocomputing covers practical aspects with contributions on advances in hardware and software development environments for neurocomputing, including, but not restricted to, simulation software environments, emulation hardware architectures, models of concurrent computation, neurocomputers, and neurochips (digital, analog, optical, and biodevices).

Neurocomputing reports on applications in different fields, including, but not restricted to, signal processing, speech processing, image processing, computer vision, control, robotics, optimization, scheduling, resource allocation and financial forecasting.

Neurocomputing publishes reviews of literature about neurocomputing and affine fields.

Neurocomputing reports on meetings, including, but not restricted to, conferences, workshops and seminars.

Neurocomputing reports on functionality/availability of software, on comparative assessments, and on discussions of neurocomputing software issues.

Now also including: Neurocomputing Letters - for the rapid publication of special short communications.

Special Issue on Deep Learning with Small Samples
Submission Date: 2020-04-15

In machine learning and computer vision fields, due to the rapid development of deep learning, recent years have witnessed breakthroughs for large-sample classification tasks. However, it remains a persistent challenge to learn a deep neural network with good generalizability from only a small number of training samples. In fact, humans can easily learn the concept of a class from a small amount of data rather than from millions of data. Moreover, many types of real-world data are small in quantity and are expensive to collect and label. Motivated by this fact, research on deep learning with small samples becomes more and more prevalent in the communities of machine learning and computer vision, for example, researches focusing on one-shot classification, few-shot classification, as well as classification with small training samples.

Recently, deep small-sample learning has achieved promising performance in certain small-sample problems, by transferring the "Knowledge" learned from other datasets containing rich labelled data or generating synthetic samples to approximate the distribution of real data. However, many challenging topics remain with small-sample deep leaning techniques, such as data augmentation, feature learning, prior construction, meta-learning, fine tuning, etc. Therefore, the goal of this special issue is to collect and publish the latest developments in various aspects of deep learning with small samples.

The list of possible topics includes, but is not limited to:

Survey/vision/review of deep learning with small samples
Data augmentation methods for small-sample leaning
Feature learning based methods small-sample leaning
Regularization technology of deep model in small-sample leaning
Ensemble learning based methods for small-sample learning
Transfer learning methods for small-sample learning
Semi-supervised learning methods for small-sample learning
Prior based methods for few-shot learning
Meta-leaning based methods for few-shot learning
Fine-tuning based methods for small-sample learning
Theoretical analysis for small-sample learning
Applications of small-sample learning on person re-identification, object recognition, etc.

Special Issue on Deep Dictionary Learning: Algorithm, Theory and Application
Submission Date: 2020-04-15

Dictionary Learning (DL) is a long-standing popular topic for visual image representation due to its great success to image restoration, de-noising and classification, etc. DL aims at representing data using a linear combination of a few highly correlated atoms in a dictionary D. But how to obtain a desired dictionary from inputs still remains a challenging task to date. It is noteworthy that most existing DL algorithms represent data using a single-layer framework, so they usually fail to obtain the deep feature representations with more useful and valuable hidden information discovered. In recent years, with the fast development of deep learning and multi-layer neural networks, it will be helpful to propose deeper or multi-layer DL frameworks for representation learning. Although certain efforts have been made to incorporate the deep learning into the DL, most designs of so-called deep dictionary learning (DDL) algorithms are still less straightforward. For example, some existing algorithms feed deep features of the deep networks into DL for representation learning, or perform the DL first and then use the reconstructed data for deep learning. As such, it is now necessary to integrate the DL with deep networks, and explore the advanced algorithms, theories and optimization approaches for the deep dictionary learning.

In this special issue, we invite contributions from diverse research fields, such as deep representation learning, image processing, and computer vision, etc., developing novel algorithms from high-dimensional data.

The topics of interest include, but are not limited to:  

Survey/vision/review of dictionary learning
Robust dictionary learning
Online dictionary learning
Deep/multi-layer dictionary learning
Convolutional dictionary learning
Structured dictionary learning
Bayesian dictionary learning
Coupled/semi-coupled dictionary learning
Optimization for dictionary learning/deep dictionary learning
Applications to image processing