Connectionists: Knowledge-Based Systems Special Issue on Robust, Explainable, and Privacy-Preserving Deep Learning

[Pub Conference]

Robust, Explainable, and Privacy-Preserving Deep Learning
https://www.journals.elsevier.com/knowledge-based-systems/call-for-papers/robust-explainable-and-privacy-preserving-deep-learning

*Aim and Scope*

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 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, natural language processing, and medical
communications. However, deep neural networks yield ‘black-box’
input-output mappings that can be challenging to explain to users.
Especially in the healthcare, cybersecurity, 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. Further, despite their enormous societal benefits, deep
learning can pose real threats to personal privacy. For example, deep
neural networks and other machine learning models are built based on
patients' personal and highly sensitive data such as clinical records or
tracked health data in the domain of healthcare. Moreover, they can be
vulnerable to attackers trying to infer the sensitive data that was used to
build the model. This raises important research questions about how to
develop deep learning models that protect private data against inference
attacks while still being accurate and useful predictive models.

This Special Issue will present robust, explainable, and efficient
next-generation deep learning algorithms with data privacy and theoretical
guarantees for solving challenging artificial intelligence problems. This
Special Issue aims to: 1) improve the understanding and explainability of
deep neural networks; 2) improve the accuracy of deep learning leveraging
new stochastic optimization and neural architecture search; 3) enhance the
mathematical foundation of deep neural networks; 4) design new data privacy
mechanisms to optimally tradeoff between utility and privacy; and 5)
increase the computational efficiency and stability of the deep learning
training process with new algorithms that will scale. Potential topics
include but are not limited to the following:

·         Novel theoretical insights on the deep neural networks

·         Exploration of post-hoc interpretation methods which can shed
light on how deep learning models produce a specific prediction and
generate a representation

·         Investigation of interpretable models which aim to construct
self-explanatory models and incorporate interpretability directly into the
structure of a deep learning model

·         Quantifying or visualizing the interpretability of deep neural
networks

·         Stability improvement of deep neural network optimization

·         Optimization methods for deep learning

·         Privacy preserving machine learning (e.g., federated machine
learning, learning over encrypted data)

·         Novel deep learning approaches in the applications of
image/signal processing, business intelligence, games, healthcare,
bioinformatics, and security

*Important Dates*

·         Submission Deadline: August 31, 2021

·         First Review Decision: September 30, 2021

·         Revisions Due: October 31, 2021

·         Final Decision: November 30, 2021

·         Final Manuscript: December 31, 2021

*Dissemination, Composition and Review Procedures*

·         A Call for Papers (CFP) will be circulated to invite submissions.

·         World leading researchers will be invited as authors.

·         To further attracting contributors from around the world, the CFP
will be advertised across numerous society newsletters, different websites,
mailing lists, conferences, associations, and social media groups, etc.


This special issue will run as per the timeline given from submission to
publication, while maintaining the rigorous peer review and high standards
of the journal. All manuscripts submitted must be original, not under
consideration elsewhere, and not previously published. A guide for authors
and other relevant information for submission of manuscripts are available
on the Guide for Authors’ page. Authors can expect their manuscripts to be
reviewed fairly, and in a skilled, conscientious manner. To enhance
objectivity, and to guarantee high scientific quality and relevance to the
subject, three peer reviewers will be selected to evaluate a manuscript.
The peer review process shall be designed to avoid bias and conflict of
interest on the part of reviewers and shall be composed of experts in the
relevant field of research. A key criterion in publication decisions will
be the manuscript’s fit for the special issue and the readership of KBS.
Papers will be published online as soon as accepted in continuous flow.

*Submission Instructions*

The submission system will be open around one week before the first paper
comes in. When submitting your manuscript please select the article type “*VSI:
Deep Learning*”. Please submit your manuscript before the submission
deadline.

All submissions deemed suitable to be sent for peer review will be reviewed
by at least two independent reviewers. Once your manuscript is accepted, it
will go into production, and will be simultaneously published in the
current regular issue and pulled into the online Special Issue. Articles
from this Special Issue will appear in different regular issues of the
journal, though they will be clearly marked and branded as Special Issue
articles.

Please see an example here:
https://www.sciencedirect.com/journal/science-of-the-total-environment/special-issue/10SWS2W7VVV

Please ensure you read the Guide for Authors before writing your
manuscript. The Guide for Authors and the link to submit your manuscript is
available on the Journal’s homepage.
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