11 citations · 12 across the 9 of their papers we have counts for
4 papers · 1 filter
Multi-student Diffusion Distillation for Better One-step Generators
Yanke Song, Jonathan Lorraine, Weili Nie +2
Diffusion models achieve high-quality sample generation at the cost of a lengthy multistep inference procedure. To overcome this, diffusion distillation techniques produce student…
SpaceMesh: A Continuous Representation for Learning Manifold Surface Meshes
Tianchang Shen, Zhaoshuo Li, Marc Law +5
Meshes are ubiquitous in visual computing and simulation, yet most existing machine learning techniques represent meshes only indirectly, e.g. as the level set of a scalar field or…
Improving Hyperparameter Optimization with Checkpointed Model Weights
Nikhil Mehta, Jonathan Lorraine, Steve Masson +4
When training deep learning models, the performance depends largely on the selected hyperparameters. However, hyperparameter optimization (HPO) is often one of the most expensive p…
LATTE3D: Large-scale Amortized Text-To-Enhanced3D Synthesis
Kevin Xie, Jonathan Lorraine, Tianshi Cao +5
Recent text-to-3D generation approaches produce impressive 3D results but require time-consuming optimization that can take up to an hour per prompt. Amortized methods like ATT3D o…