15 citations · 26 across the 6 of their papers we have counts for
4 papers · 1 filter
Self-Refining Diffusion Samplers: Enabling Parallelization via Parareal Iterations
Nikil Roashan Selvam, Amil Merchant, Stefano Ermon
In diffusion models, samples are generated through an iterative refinement process, requiring hundreds of sequential model evaluations. Several recent methods have introduced appro…
Scalable Diffusion for Materials Generation
Sherry Yang, KwangHwan Cho, Amil Merchant +4
Generative models trained on internet-scale data are capable of generating novel and realistic texts, images, and videos. A natural next question is whether these models can advanc…
VeLO: Training Versatile Learned Optimizers by Scaling Up
Luke Metz, James Harrison, C. Daniel Freeman +8
While deep learning models have replaced hand-designed features across many domains, these models are still trained with hand-designed optimizers. In this work, we leverage the sam…
Learn2Hop: Learned Optimization on Rough Landscapes
Amil Merchant, Luke Metz, Sam Schoenholz +1
Optimization of non-convex loss surfaces containing many local minima remains a critical problem in a variety of domains, including operations research, informatics, and material d…