16 citations · 34 across the 21 of their papers we have counts for
9 papers · 1 filter
Heavy-Tailed Diffusion Models
Kushagra Pandey, Jaideep Pathak, Yilun Xu +4
Diffusion models achieve state-of-the-art generation quality across many applications, but their ability to capture rare or extreme events in heavy-tailed distributions remains unc…
Warped Diffusion: Solving Video Inverse Problems with Image Diffusion Models
Giannis Daras, Weili Nie, Karsten Kreis +4
Using image models naively for solving inverse video problems often suffers from flickering, texture-sticking, and temporal inconsistency in generated videos. To tackle these probl…
Stochastic Flow Matching for Resolving Small-Scale Physics
Stathi Fotiadis, Noah Brenowitz, Tomas Geffner +4
Conditioning diffusion and flow models have proven effective for super-resolving small-scale details in natural images.However, in physical sciences such as weather, super-resolvin…
Kilometer-Scale Convection Allowing Model Emulation using Generative Diffusion Modeling
Jaideep Pathak, Yair Cohen, Piyush Garg +8
Storm-scale convection-allowing models (CAMs) are an important tool for predicting the evolution of thunderstorms and mesoscale convective systems that result in damaging extreme w…
Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales
Peter Manshausen, Yair Cohen, Peter Harrington +7
Data assimilation of observational data into full atmospheric states is essential for weather forecast model initialization. Recently, methods for deep generative data assimilation…
Repulsive Latent Score Distillation for Solving Inverse Problems
Nicolas Zilberstein, Morteza Mardani, Santiago Segarra
Score Distillation Sampling (SDS) has been pivotal for leveraging pre-trained diffusion models in downstream tasks such as inverse problems, but it faces two major challenges: $(i)…
Compositional Text-to-Image Generation with Dense Blob Representations
Weili Nie, Sifei Liu, Morteza Mardani +3
Existing text-to-image models struggle to follow complex text prompts, raising the need for extra grounding inputs for better controllability. In this work, we propose to decompose…
DiffObs: Generative Diffusion for Global Forecasting of Satellite Observations
Jason Stock, Jaideep Pathak, Yair Cohen +5
This work presents an autoregressive generative diffusion model (DiffObs) to predict the global evolution of daily precipitation, trained on a satellite observational product, and…
AGG: Amortized Generative 3D Gaussians for Single Image to 3D
Dejia Xu, Ye Yuan, Morteza Mardani +4
Given the growing need for automatic 3D content creation pipelines, various 3D representations have been studied to generate 3D objects from a single image. Due to its superior ren…