Connectionists: Call for Paper - Computational Intelligence and Neuroscience Special Issue

[Pub Conference]

Interpretation of Machine Learning: Prediction, Representation, Modeling,
and Visualization 2022
https://www.hindawi.com/journals/cin/si/470979/

Call for papers

This Issue is now open for submissions.

Papers are published upon acceptance, regardless of the Special Issue
publication date.
------------------------------

<https://www.hindawi.com/journals/cin/si/470979/>

Description <https://www.hindawi.com/journals/cin/si/470979/>



The exponentially growing availability of data such as images, videos and
speech from myriad sources, including social media and the Internet of
Things, is driving the demand for high-performance data analysis
algorithms. Deep learning is currently an extremely active research area in
machine learning and pattern recognition. It provides computational models
of multiple nonlinear processing neural network layers to learn and
represent data with increasing levels of abstraction. Deep neural networks
are able to implicitly capture intricate structures of large-scale data and
deploy them in cloud computing and high-performance computing platforms.

The deep learning approach has demonstrated remarkable performances across
a range of applications, including computer vision, image classification,
face/speech recognition and medical communications. However, deep neural
networks yield ‘black-box’ input-output mappings that can be challenging to
explain to users. Especially in the medical, military and legal fields,
black-box machine learning techniques are unacceptable, since decisions may
have a profound impact on peoples’ lives due to the lack of
interpretability. In addition, many other open problems and challenges
still exist, such as computational and time costs, repeatability of the
results, convergence, and the ability to learn from a very small amount of
data and to evolve dynamically.

The aim of this Special Issue is to bring together original research
articles and review articles that will present the latest theoretical and
technical advancements of machine and deep learning models. Submissions
about algorithms with improved computational efficiency and scalability are
also welcome. We hope that this Special Issue will: 1) improve the
understanding and explainability of deep neural networks; 2) enhance the
mathematical foundation of deep neural networks; and 3) increase the
computational efficiency and stability of the machine and deep learning
training process with new algorithms that will scale.

Potential topics include but are not limited to the following:

   - Supervised, unsupervised, and reinforcement learning
   - Classification, clustering, and optimization for big data analytics
   - Extracting understanding from large-scale and heterogeneous data
   - Dimensionality reduction and analysis of large-scale and complex data
   - Deep learning for time series forecasting
   - Quantifying or visualizing the interpretability of deep neural networks
   - Stability improvement of deep neural network optimization
   - Novel machine and deep learning approaches in the applications of
   image/signal processing, business intelligence, games, healthcare,
   bioinformatics, and security


Publishing date
01 Dec 2022
Status
Open

Submission deadline
22 Jul 2022
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/connectionists/attachments/20220414/4e54faba/attachment.html>