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Algorithms
Special Issues
Recent Advances in Machine Learning Algorithms and Applications
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Recent Advances in Machine Learning Algorithms and Applications

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 1931

Special Issue Editors

Dr. Uche Onyekpe

E-Mail Website
Guest Editor
School of Science, Technology and Health, York St John University, York YO31 7EX, UK
Interests: deep learning; machine learning; artificial intelligence
Dr. Yordanka Karayaneva

E-Mail Website
Guest Editor
Department of Computing & Games, Teesside University, Middlesbrough TS1 3BX, UK
Interests: machine learning; signal processing; object recognition; computer vision
Prof. Dr. Vasile Palade

E-Mail Website
Guest Editor
Research Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry, UK
Interests: artificial Intelligence; data science; machine learning; computational intelligence; neural networks; deep learning; neuro-fuzzy systems; various nature-inspired algorithms
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mohd Arif Wani

E-Mail Website
Guest Editor
Department of Computer Science, University of Kashmir, Srinagar 190006, India
Interests: artificial intelligence; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This cooperative Special Issue, conducted in collaboration with the 21st IEEE International Conference on Machine Learning and Applications (https://www.icmla-conference.org/icmla22/), presents a platform for original research in Machine Learning (ML) with an emphasis on applications and innovative algorithms and systems. We welcome submissions from a diverse range of ML-related fields, which may include but are not limited to theoretical research, algorithms, and their practical applications. The emergence of Big Data processing has created an urgent need for novel machine learning algorithms to tackle new challenges. We encourage contributions describing cutting-edge machine-learning techniques and algorithms applied to real-world problems, as well as interdisciplinary research employing machine learning in fields such as medicine, biology, industry, manufacturing, security, education, virtual environments, and gaming.

Dr. Uche Onyekpe
Dr. Yordanka Karayaneva
Prof. Dr. Vasile Palade
Prof. Dr. Mohd Arif Wani
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

  • machine learning
  • evolutionary algorithms
  • neural network learning
  • reinforcement learning
  • machine learning and information retrieval
  • machine learning on the edge
  • hybrid learning algorithms

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Published Papers (1 paper)

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Research

19 pages, 1107 KiB  
Article
Variable Scale Pruning for Transformer Model Compression in End-to-End Speech Recognition
by Leila Ben Letaifa and Jean-Luc Rouas
Algorithms 2023, 16(9), 398; https://doi.org/10.3390/a16090398 - 23 Aug 2023
Cited by 1 | Viewed by 1292
Abstract
Transformer models are being increasingly used in end-to-end speech recognition systems for their performance. However, their substantial size poses challenges for deploying them in real-world applications. These models heavily rely on attention and feedforward layers, with the latter containing a vast number of [...] Read more.
Transformer models are being increasingly used in end-to-end speech recognition systems for their performance. However, their substantial size poses challenges for deploying them in real-world applications. These models heavily rely on attention and feedforward layers, with the latter containing a vast number of parameters that significantly contribute to the model’s memory footprint. Consequently, it becomes pertinent to consider pruning these layers to reduce the model’s size. In this article, our primary focus is on the feedforward layers. We conduct a comprehensive analysis of their parameter count and distribution. Specifically, we examine the weight distribution within each layer and observe how the weight values progress across the transformer model’s blocks. Our findings demonstrate a correlation between the depth of the feedforward layers and the magnitude of their weights. Consequently, layers with higher weight values require less pruning. Building upon this insight, we propose a novel pruning algorithm based on variable rates. This approach sets the pruning rate according to the significance and location of each feedforward layer within the network. To evaluate our new pruning method, we conduct experiments on various datasets. The results reveal its superiority over conventional pruning techniques, such as local pruning and global pruning. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning Algorithms and Applications)
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