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.