Search Results for author:
Found 7 papers, 5 papers with code
All-in-one simulation-based inference
Amortized Bayesian inference trains neural networks to solve stochastic inference problems using model simulations, thereby making it possible to rapidly perform Bayesian inference for any newly observed data.
If the Sources Could Talk: Evaluating Large Language Models for Research Assistance in History
We demonstrate that LLMs semantic retrieval and reasoning abilities on problem-specific tasks can be applied to large textual archives that have not been part of the its training data.
Graphically Structured Diffusion Models
We introduce a framework for automatically defining and learning deep generative models with problem-specific structure.
Flexible Diffusion Modeling of Long Videos
We present a framework for video modeling based on denoising diffusion probabilistic models that produces long-duration video completions in a variety of realistic environments.
Inferring the Structure of Ordinary Differential Equations
Understanding physical phenomena oftentimes means understanding the underlying dynamical system that governs observational measurements.
Planning as Inference in Epidemiological Models
In this work we demonstrate how to automate parts of the infectious disease-control policy-making process via performing inference in existing epidemiological models.
Efficient Inference Amortization in Graphical Models using Structured Continuous Conditional Normalizing Flows
We introduce a more efficient neural architecture for amortized inference, which combines continuous and conditional normalizing flows using a principled choice of structure.
