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International Journal of Machine Learning and Cybernetics - Call for Papers: Pretraining: Foundation, Frontiers and Applications

A Special Issue in the International Journal of Machine Learning and Cybernetics
Open for submissions until August 31, 2024


The era of large models is coming. Within the disruptive innovations of large models, pretraining is showing fundamental importance in a wider range of intelligent applications, ranging from BERT and ChatGPT to recommender system, computer vision, etc. “Pretraining-Prompting” becomes an emerging paradigm in the next generation deep learning technologies. Recent years have witnessed the success of pretraining in large language models, graphs/social networks models, vision models, etc. However, several important issues remain open, e.g., the efficiency and effectiveness of pretraining with limited data, privacy or machine unlearning in pretraining, and novel applications. There is seldom a special issue focused on all aspects of pretraining, and we are committed to bridging this gap and advancing pretraining for broader applications.

This special issue features original studies on pretraining, including fundamental and cutting-edge theories and applications on language models, graphs/social networks, computer vision, knowledge graphs, and recommender systems.

Topics of interest

Topics of interest include, but are not limited to, the following:

  • Pretraining and Self-supervised Learning Methods
  • Lightweight Deep Learning and Knowledge Distillation
  • Privacy and Machine Unlearning in Pretraining Federated Pretraining Technology
  • Pretraining with Limited Resources
  • Cross-Modal/Explainable LLMs
  • Pretraining and Prompting Heterogenous/Heterophily Graphs
  • Pretraining Graphs in Geometric/Riemannian Spaces
  • Pretraining and Prompting Methods for Knowledge Graphs 
  • Knowledge Probing from Pretraining Model
  • Pretraining for Controlable Text Generation/ Summarization
  • Pretraining on Machine Translation 
  • Pretraining on Recommender Systems
  • Video-Language Pretraining
  • Speech Pretraining for Speech Synthesis and Recognition


SUBMISSION - RELEVANT INFORMATION AND GUIDELINES


  • All papers will be peer-reviewed. Before any special issue is given final approval to be put into production, additional rigorous integrity checks are carried out by the Editor-in-Chief, Special Issues Assistant Editor, Editorial Team, Production Office and by Springer Nature.



  • Submissions should be original papers and should not be under consideration for publication elsewhere.


GUEST EDITORS:


Li Sun (LEAD GUEST EDITOR) - is currently an Assistant Professor at School of Control and Computer Engineering, North China Electric Power University, Beijing, China. He received the Ph.D. of Computer Science from Beijing University of Posts and Telecommunications. Dr. Sun's research interests lie in data mining and machine learning, and specifically, his recent works focus on graph neural networks, social network analysis, Riemannian graph learning, etc. Dr. Sun was the recipient of CIKM 2022 best paper winners (honorable mention), and has published over 30 refereed top conference and journal papers, including ACM The Web Conference (WWW), ACM International Conference on Information and Knowledge Management (CIKM), AAAI Conference on Artificial Intelligence (AAAI), International Joint Conference on Artificial Intelligence (IJCAI), IEEE International Conference on Data Mining (ICDM), IEEE Transactions on Knowledge and Data Engineering (TKDE), ACM Transactions on the Web (TWEB), ACM Transactions on Intelligent System and Technology (TIST), etc. Dr. Sun has been invited to serve as the Program Committee member in AAAI 2024, SIGKDD 2023, AAAI 2023, SIGKDD 2022, WSDM 2022, AAAI 2022, AAAI 2021, IJCAI 2021, ECAI 2020, etc. Dr. Sun has been invited to serve as the Reviewer for JMLC, ACM TWEB, ACM TIST, etc., Guest Editor in Electronics, Publicity Chair of IEEE SocialCom’23. Email: ccesunli@ncepu.edu.cn

Hao Peng - is currently a Professor at Beijing Advanced Innovation Center for BigData and Brain Computing,Beihang University. His research interests include representation learning, machine learning and graph mining. He has published 100+ refereed journal and conference papers, including IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE Transactions on Computers (TC), IEEE Transactions on Parallel and Distributed Systems (TPDS), IEEE Transactions on Neural Networks and Learning Systems (TNNLS), IEEE Transactions on Industrial Informatics (TII), IEEE Transactions on Intelligent Transportation Systems (TITS), IEEE Transactions on Audio, Speech, and Language Processing (TASLP), ACM Transactions on Information Systems (TOIS), ACM Transactions on Knowledge Discovery Data (TKDD), ACM Transactions on Intelligent Systems and Technology (TIST), The ACM Web Conference (WWW), International Conference on Research and Development in Information Retrieval (SIGIR), Conference on Neural Information Processing Systems (NeurIPS), AAAI Conference on Artificial Intelligence (AAAI), International Joint Conference on Artificial Intelligence (IJCAI), ACM International Conference on Information and Knowledge Management (CIKM), International Conference on Data Mining (ICDM), International Conference on Database Systems for Advanced Applications (DASFAA), International Conference on Computational Linguistics (COLING), and North American Chapter of the Association for Computational Linguistics (NAACL). Prof. Peng also serves as the Associate Editor of the International Journal of Machine Learning and Cybernetics, Guest Co-Chair in Deep Learning for Anomaly Detection: Theory, Algorithms, and Applications in IJCNN2022, and IJCNN2023, Leading Guest Editor in ACM Transactions on the Web, Leading Guest Editor in IEEE Transaction on Knowledge and Data Engineering (TKDE), Leading Guest Editor:ACM Transactions on Intelligent Systems and Technology (TIST). Email: penghao@buaa.edu.cn

Zhiwei Liu - is currently a research scientist in Salesforce research. He received a Ph.D. degree from University of Illinois at Chicago. Dr. Liu's research interests lie in large language models and data mining. Specifically, those research works on dialog system, graph neural networks, recommender systems etc. Dr. Liu has published over 40 original research works, including ACM WWW, ACM SIGIR, ACM CIKM, ACM WSDM, ACM TIST, EMNLP, ICML, SIGDIAL etc. Dr. Liu served as the program committee members in KDD, CIKM, NeurIPS, ACM Web Conference, WSDM, etc. And Dr. Liu also served as the leading guest editor at ACM TORS Special Issue on KT4Rec. Email: zhiweiliu@salesforce.com

Ran Wang - is currently an Associate Professor (Tenured) at School of Mathematical Sciences, Shenzhen University, China. She received the Ph.D. degree of Computer Science from City University of Hong Kong. Prof. Wang's research interests lie in machine learning and pattern recognition. Prof. Wang has published 60+ refereed papers with 2000 citations in top conference and journal papers, including IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, IEEE Transactions on Cybernetics, etc. She has been invited to serve as the reviewers of a number of journals. Email: wangran@szu.edu.cn

Lifang He - is currently an Assistant Professor in the Department of Computer Science and Engineering at Lehigh University. Her primary research interests focus on machine learning, artificial intelligence, and their applications in medical data mining. Dr. He has published more than 150 papers in refereed journals and conferences, with an H-index of 38 and more than 4000 citations. In addition to her academic accomplishments, Dr. He actively contributes to the scientific community as a reviewer and program committee member for various esteemed journals and conferences. Some notable ones include IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), IEEE Transactions on Neural Networks and Learning Systems (TNNLS), IEEE Transactions on Knowledge and Data Engineering (TKDE), Neural Information Processing Systems (NeurIPS), International Conference on Machine Learning (ICML), ACM Conference of Knowledge Discovery and Data Mining (SIGKDD), Association for the Advancement of Artificial Intelligence (AAAI), and Computer Vision and Pattern Recognition (CVPR). Furthermore, she has been serving as the chair of the Computer Science Chapter at the IEEE Lehigh Valley Section since 2023 January. Email: lih319@lehigh.edu

Jia Wu - is currently the Research Director for the Centre for Applied Artificial Intelligence and the Director of HDR (Higher Degree Research) in the School of Computing at Macquarie University, Sydney, Australia. Since 2009, Dr. Wu has published 100+ refereed journal and conference papers, including IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE Transactions on Multimedia (TMM), IEEE Transactions on Industrial Informatics (TII), IEEE Transactions on Neural Networks and Learning Systems (TNNLS), International Joint Conference on Artificial Intelligence (IJCAI), AAAI Conference on Artificial Intelligence (AAAI), ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), IEEE International Conference on Data Mining (ICDM), The Web Conference (WWW), and Neural Information Processing Systems (NeurIPS). Dr Wu has been serving as the PC Chair/Tutorial Chair/Demo Chair/ Contest Chair/Publicity Chair/(Senior) PC members for the prestigious data mining and artificial intelligence conferences for over 10 years, such as KDD, ICDM, WSDM, IJCAI, AAAI, WWW, NIPS, CIKM, SDM, etc. His research team was the recipient of ICDM'21 Best Student Paper Award, SDM'18 Best Paper Award in Data Science Track, IJCNN'17 Best Student Paper Award, and ICDM'14 Best Paper Candidate Award. Dr. Wu is the Associate Editor of the ACM Transactions on Knowledge Discovery from Data (TKDD), a top data mining journal, and the Associate Editor of Neural Networks. Dr Wu is a Senior Member of the IEEE. Email: jia.wu@mq.edu.cn

Julian Mcauley - is currently a Professor in the Computer Science Department at the University of California, San Diego. Prof. Julian McAuley received his Ph.D. degree from the Australian National University, Canberra, ACT, Australia, in 2011.He has been a Professor with the Computer Science Department, University of California, San Diego, La Jolla, CA, USA, since 2014, where he works on applications of machine learning to problems involving personalization and teaches classes on the personalized recommendation. He likes bicycling and baroque keyboard. Previously, he was a Postdoctoral Scholar with Stanford University, Stanford, CA, USA. His research is concerned with developing predictive models of human behavior using large volumes of online activity data. He has published more than 150 original works in Advances in Neural Information Processing Systems (NeurIPS), ACM SIGIR, ACM RecSys, ACM world wide web, IEEE International Conference on Data Mining (ICDM). Email: jmcauley@eng.ucsd.edu

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