讲座内容：Exploring the Frontiers of Deep Generative Modeling: Innovations in Data Synthesis and Unsupervised Learning （探索深度生成建模的前沿：数据合成与无监督学习的创新）
讲座人：Hamido Fujita教授
讲座时间：11月19日 14:00-16:00
腾讯会议：409-8117-7679
Abstract：
In the rapidly evolving field of artificial intelligence, Deep Generative Modeling (DGM) has emerged as a transformative approach, capable of generating high-quality, synthetic data across various domains. Despite the impressive capabilities of deep learning in tasks such as classification and regression, generating realistic and diverse data has remained a significant challenge. DGM addresses this gap by learning complex data distributions and generating samples that closely mimic real-world data. The core idea of DGM is to leverage deep neural networks, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), to model and generate data without relying on labeled training examples. By learning latent representations of the data, these models can produce new data points that exhibit the same statistical properties as the original data, making them invaluable for applications such as data augmentation, creative content generation, and unsupervised learning. At the same time, DGM techniques face challenges such as mode collapse, training instability, and the ethical implications of synthetic data. Nevertheless, ongoing advancements in DGM are pushing the boundaries of what is possible in data synthesis, offering exciting opportunities for innovation in both research and industry. This approach promises to reshape the landscape of AI by enabling machines to generate data that is indistinguishable from real-world observations, thereby enhancing model training and expanding the potential of AI applications.
Short Bio：
He is Executive Chairman of i-SOMET Incorporated Association, Japan, and Distinguished Professor at Iwate Prefectural University, Japan, he is also Research Professor at University of Granada, Spain. He is Highly Cited Researcher in CrossField for the year 2019 and in Computer Science for the year 2020, 2021 and 2022, by Clarivate Analytics. He received Doctor Honoris Causa from Óbuda University, Budapest, Hungary, in 2013 and received Doctor Honoris Causa from Timisoara Technical University, Timisoara, Romania, in 2018, and a title of Honorary Professor from Óbuda University, in 2011. He is Distinguished Research Professor at the University of Granada, and Adjunct Professor with Taipei Technical University, Taiwan, Harbin Engineering University, China and others. He supervised Ph.D. students jointly with the University of Laval, Quebec City, QC, Canada; University of Technology Sydney; Oregon State University, Corvallis, OR, USA; University of Paris 1 Pantheon-Sorbonne, Paris, France; and University of Genoa, Italy. Dr. Fujita is the recipient of the Honorary Scholar Award from the University of Technology Sydney, in 2012. He was the Editor-in-Chief for Knowledge-Based Systems (Elsevier) (2005- 2019) and then Emeritus Editor of Knowledge-Based Systems in 2020~. Since 2020 he is currently the Editor-in-Chief of Applied Intelligence (Springer) and the Editor-inChief of International Journal of Healthcare Management (Taylor & Francis). He headed a number of projects including intelligent HCI, a project related to mental cloning for healthcare systems as an intelligent user interface between human-users and computers, and SCOPE project on virtual doctor systems for medical applications. He collaborated with several research projects in Europe, and recently he is collaborating in OLIMPIA project supported by Tuscany region on Therapeutic monitoring of Parkison disease. He has published more than 400 articles mainly in high impact factor journals.
