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 4521

Special Issue Editors


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Guest Editor
School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand
Interests: intelligent computation; knowledge engineering; semantic web; bioinformatics

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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
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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

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Keywords

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

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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
Cited by 2 | Viewed by 3082
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
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