Deep Generative Model in Machine Learning: Theory, Principle and Efficacy

Wei Huang, Mingyuan Bai, Andi Han, Taiji Suzuki, Qibin Zhao, Bamdev Mishra, Denny Wu, Ye Yuan, Maud Lemercier, Ernest K. Ryu

Keywords: Deep Generative Models, Diffusion Models, GAN, flow, Flow-based generative model, VAE, learning theory

Abstract: Deep Generative Models (DGMs) have significantly advanced artificial intelligence (AI) through innovations like variational autoencoders, flow-based models, generative adversarial networks, and diffusion models. Despite their success, substantial theoretical and practical challenges remain, including the lack of rigorous theoretical frameworks, training instability, scalability issues, and challenges in adapting to structured domains. This workshop aims to bridge the gap between theory and practice by addressing two key questions: (1) How to develop comprehensive theoretical frameworks for DGMs? (2) How to develop principled strategies to improve the practical efficiency, reliability and transferability of DGMs in real-world applications? By bringing together experts from diverse backgrounds, the workshop will foster interdisciplinary collaboration to develop principled solutions, ultimately advancing the theoretical foundations and practical efficacy of DGMs.

Submission Number: 39