372 citations · 904 across the 25 of their papers we have counts for
28 papers
Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling
Morteza Mardani, Noah Brenowitz, Yair Cohen +10
The state of the art for physical hazard prediction from weather and climate requires expensive km-scale numerical simulations driven by coarser resolution global inputs. Here, a g…
Recurrence without Recurrence: Stable Video Landmark Detection with Deep Equilibrium Models
Paul Micaelli, Arash Vahdat, Hongxu Yin +2
Cascaded computation, whereby predictions are recurrently refined over several stages, has been a persistent theme throughout the development of landmark detection models. In this…
Tackling the Generative Learning Trilemma with Denoising Diffusion GANs
Zhisheng Xiao, Karsten Kreis, Arash Vahdat
A wide variety of deep generative models has been developed in the past decade. Yet, these models often struggle with simultaneously addressing three key requirements including: hi…
AdaViT: Adaptive Tokens for Efficient Vision Transformer
Hongxu Yin, Arash Vahdat, Jose Alvarez +3
We introduce A-ViT, a method that adaptively adjusts the inference cost of vision transformer (ViT) for images of different complexity. A-ViT achieves this by automatically reducin…
Score-Based Generative Modeling with Critically-Damped Langevin Diffusion
Tim Dockhorn, Arash Vahdat, Karsten Kreis
Score-based generative models (SGMs) have demonstrated remarkable synthesis quality. SGMs rely on a diffusion process that gradually perturbs the data towards a tractable distribut…
Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence
Tianshi Cao, Alex Bie, Arash Vahdat +2
Although machine learning models trained on massive data have led to break-throughs in several areas, their deployment in privacy-sensitive domains remains limited due to restricte…