43 citations · 77 across the 15 of their papers we have counts for
16 papers · 1 filter
Variance Reduction for Expectations with Diffusion Teachers
Jesse Bettencourt, Xindi Wu, Matan Atzmon +2
Pretrained diffusion models serve as frozen teachers feeding downstream pipelines such as text-to-3D, single-step distillation, and data attribution. The teacher gradients these pi…
LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models
Zhengyi Wang, Jonathan Lorraine, Yikai Wang +4
This work explores expanding the capabilities of large language models (LLMs) pretrained on text to generate 3D meshes within a unified model. This offers key advantages of (1) lev…
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…
JacNet: Learning Functions with Structured Jacobians
Jonathan Lorraine, Safwan Hossain
Neural networks are trained to learn an approximate mapping from an input domain to a target domain. Incorporating prior knowledge about true mappings is critical to learning a use…
Scalable Nested Optimization for Deep Learning
Jonathan Lorraine
Gradient-based optimization has been critical to the success of machine learning, updating a single set of parameters to minimize a single loss. A growing number of applications re…
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…