92 citations · 148 across the 4 of their papers we have counts for
6 papers
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…
Forecasting Black Sigatoka Infection Risks with Latent Neural ODEs
Yuchen Wang, Matthieu Chan Chee, Ziyad Edher +4
Black Sigatoka disease severely decreases global banana production, and climate change aggravates the problem by altering fungal species distributions. Due to the heavy financial b…
Learning Differential Equations that are Easy to Solve
Jacob Kelly, Jesse Bettencourt, Matthew James Johnson +1
Differential equations parameterized by neural networks become expensive to solve numerically as training progresses. We propose a remedy that encourages learned dynamics to be eas…
DiffEqFlux.jl - A Julia Library for Neural Differential Equations
Chris Rackauckas, Mike Innes, Yingbo Ma +3
DiffEqFlux.jl is a library for fusing neural networks and differential equations. In this work we describe differential equations from the viewpoint of data science and discuss the…
FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models
Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt +2
A promising class of generative models maps points from a simple distribution to a complex distribution through an invertible neural network. Likelihood-based training of these mod…
Neural Ordinary Differential Equations
Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt +1
We introduce a new family of deep neural network models. Instead of specifying a discrete sequence of hidden layers, we parameterize the derivative of the hidden state using a neur…