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Algorithms
Special Issues
Autonomous Learning Systems: Concepts, Methodologies, and Applications
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Autonomous Learning Systems: Concepts, Methodologies, and Applications

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (1 February 2024) | Viewed by 2910

Special Issue Editors

Dr. Xingsi Xue

E-Mail Website
Guest Editor
School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand
Interests: intelligent computation; knowledge engineering; semantic web; bioinformatics
Dr. Pei-Wei Tsai

E-Mail Website
Guest Editor
Department of Computer Science and Software Engineering, Swinburne University of Technology, Mail No. H39, P.O. Box 218, Hawthorn, VIC 3122, Australia
Interests: optimization and workflow management; machine learning; data analytics; city logistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Autonomous learning systems at the forefront of machine learning and artificial intelligence advancements. These systems, including but not limited to self-learning algorithms, have the potential to learn and improve their performance over time with minimal supervision. They are increasingly applied across a diverse array of domains, from image recognition and natural language processing to healthcare diagnostics and financial predictions. Despite the significant advancements, numerous research challenges persist in the field of autonomous learning systems. How can we design systems that mitigate the risk of confirmation bias, ensure ethical use given their potential autonomy, and address scalability issues in the face of today's big data era?

The aim of this Special Issue is to provide a dedicated platform for researchers and practitioners to share their latest advancements, insights, and experiences in the development, application, and implications of autonomous learning systems. We encourage contributions addressing innovative concepts, methodologies, real-world applications, and ethical considerations in this exciting and rapidly evolving field.

Dr. Xingsi Xue
Dr. Pei-Wei Tsai
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • autonomous learning systems
  • artificial intelligence
  • machine learning
  • self-learning algorithms

Published Papers (1 paper)

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Research

23 pages, 1212 KiB  
Article
Activation-Based Pruning of Neural Networks
by Tushar Ganguli and Edwin K. P. Chong
Algorithms 2024, 17(1), 48; https://doi.org/10.3390/a17010048 - 21 Jan 2024
Viewed by 1789
Abstract
We present a novel technique for pruning called activation-based pruning to effectively prune fully connected feedforward neural networks for multi-object classification. Our technique is based on the number of times each neuron is activated during model training. We compare the performance of activation-based [...] Read more.
We present a novel technique for pruning called activation-based pruning to effectively prune fully connected feedforward neural networks for multi-object classification. Our technique is based on the number of times each neuron is activated during model training. We compare the performance of activation-based pruning with a popular pruning method: magnitude-based pruning. Further analysis demonstrated that activation-based pruning can be considered a dimensionality reduction technique, as it leads to a sparse low-rank matrix approximation for each hidden layer of the neural network. We also demonstrate that the rank-reduced neural network generated using activation-based pruning has better accuracy than a rank-reduced network using principal component analysis. We provide empirical results to show that, after each successive pruning, the amount of reduction in the magnitude of singular values of each matrix representing the hidden layers of the network is equivalent to introducing the sum of singular values of the hidden layers as a regularization parameter to the objective function. Full article
(This article belongs to the Special Issue Autonomous Learning Systems: Concepts, Methodologies, and Applications)
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