CALL FOR PAPERS

ACM Transactions on Intelligent Systems and Technology
Special Issue on Transformers

https://dl.acm.org/pb-assets/static_journal_pages/tist/pdf/ACM-TIST-CFP-SI-Transformers-1719857985893.pdf

Editor-in-Chief: Huan Liu, Arizona State University, USA

Guest Editors:
• Feng Xia, RMIT University, Australia
• Tyler Derr, Vanderbilt University, USA
• Luu Anh Tuan, Nanyang Technological University, Singapore
• Richa Singh, IIT Jodhpur, India
• Aline Villavicencio, University of Exeter, United Kingdom

Transformer-based models have emerged as a cornerstone of modern artificial intelligence (AI), reshaping the landscape of machine learning and driving unprecedented progress in a myriad of tasks. Originating from the domain of natural language processing, transformers have transcended their initial applications to become ubiquitous across diverse fields including anomaly detection, computer vision, speech recognition, recommender systems, question answering, robotics, healthcare, education, and more. The impact of transformer models extends far beyond their technical intricacies. For instance, advanced transformers have been successfully applied to multimodal learning tasks, where they can seamlessly integrate information from different modalities such as text, images, audio, and video. This ability opens up new avenues for research in areas like visual question answering, image captioning, and video understanding.

Despite their remarkable success, however, several challenges remain. For example, training large transformer models often requires significant computational resources. Researchers are actively exploring efficient training methods, such as pre-training on massive datasets and knowledge distillation techniques, to address these limitations. Additionally, fostering explainability in transformer models is crucial for understanding their decision-making processes and building trust in real-world applications.

As transformers continue to evolve and permeate various sectors of AI, it becomes increasingly imperative to explore their advancements and applications comprehensively. This special issue seeks to provide a platform for researchers to showcase the latest developments, challenges, and opportunities in the field of transformers across diverse domains, fostering interdisciplinary dialogue and innovation.

Topics
This special issue invites contributions covering a wide range of topics related to advances in transformers. Topics of interest include, but are not limited to:
• Novel architectures and variations of transformer models
• Theoretical insights into transformers
• Efficient training and deployment of large-scale transformer models
• Fine-tuning strategies for pre-trained transformer models
• Interpretability and explainability of transformers
• Trustworthy, safe, and responsible transformers
• Transformers for diverse machine learning tasks
• Transformers for science
• Transformer-based approaches for multimodal learning
• Transformer foundation models and transformer-based generative AI
• Applications of transformers in various domains such as healthcare, education, robotics, etc.
• Ethical considerations and societal impacts of transformer technology

Important Dates
• Submissions deadline: December 1, 2024
• Tentative publication: September 2025

Submission Information
Submissions must be prepared according to the TIST submission guidelines (https://dl.acm.org/journal/tist/author-guidelines) and must be submitted via Manuscript Central (https://mc.manuscriptcentral.com/tist).
Early submissions are encouraged/preferred. We will start the review process as soon as we receive a submission.

For questions and further information, please contact Prof. Feng Xia (feng.xia@rmit.edu.au; f.xia@ieee.org).