Workshop on Diffusion Models

Workshop at the Conference on Neural Information Processing Systems (NeurIPS) 2023

Over the past three years, diffusion models have established themselves as a new generative modelling paradigm. Their empirical successes have broadened the applications of generative modelling to image, video, audio, 3D synthesis and science applications. As diffusion models become more and more popular and are applied to extremely diverse problems, it also becomes harder to track the key contributions in the field. This workshop aims to keep track of the recent advances and set guidelines for future research. By bringing together practice, methodology and theory actors we aim at identifying unexplored areas and pushing the frontier of diffusion model research.

We are currently accepting submissions! Check out our Call for Papers for more details.



Keynote Speakers

Yang Song

OpenAI

Tali Dekel

Weizmann Institute of Science, Google

Sayak Paul

Hugging Face

Brian Trippe

Columbia University

Jason Yim

MIT


Lightning Talks

Holden Lee

John Hopkins University

Hyungjin Chung

KAIST

Shuang Li

MIT

Gowthami Somepalli

University of Maryland


Panel

Arash Vahdat

NVIDIA Research

Ruiqi Gao

Google

Tim Salimans

Google

Robin Rombach

Stability AI

Organizers

Valentin De Bortoli

ENS Paris

Charlotte Bunne

ETH Zurich

Bahjat Kawar

Technion

Chenlin Meng

Stanford

Jiaming Song

Luma AI

James Thornton

Oxford University

Jong Chul Ye

KAIST


Questions

Contact us at diffusion.workshop.2023@gmail.com.