Our guest speaker is Karsten Kreis from NVIDIA’s Toronto AI Lab.

You are all cordially invited to the CVG Seminar on the 22nd of December at 5:30 pm CET

• via Zoom (passcode is 052316).

Abstract

Denoising diffusion-based generative models have led to multiple breakthroughs in deep generative learning. In this talk, I will discuss recent works by the NVIDIA Toronto AI Lab on diffusion models. In the first part, I will present GENIE: Higher-Order Denoising Diffusion Solvers, a novel method for accelerated sampling from diffusion models, leveraging higher-order methods together with an efficient model distillation technique to solve the generative differential equations of diffusion models. Next, I will discuss our work on Critically-Damped Langevin Diffusion. Taking inspirations from statistical mechanics and Markov chain Monte Carlo, we introduce an auxiliary velocity variable into the diffusion process, which allows the diffusion to converge to the Gaussian prior more smoothly and quickly. This makes critically-damped Langevin diffusion ideally suited for diffusion-based generative modeling. Finally, I will briefly recapitulate Latent Score-based Generative Models and then present LION: Latent Point Diffusion Models for 3D Shape Generation, which achieves state-of-the-art 3D shape synthesis and enables various artistic applications, such as voxel-guided shape generation.

Bio

Karsten Kreis is a senior research scientist at NVIDIA’s Toronto AI Lab. Prior to joining NVIDIA, he worked on deep generative modeling at D-Wave Systems and co-founded Variational AI, a startup utilizing generative models for drug discovery. Before switching to deep learning, Karsten did his M.Sc. in quantum information theory at the Max Planck Institute for the Science of Light and his Ph.D. in computational and statistical physics at the Max Planck Institute for Polymer Research. Currently, Karsten's research focuses on developing novel generative learning methods and on applying deep generative models on problems in areas such as computer vision, graphics and digital artistry, as well as in the natural sciences.