research
∙
05/22/2023
Gibbs free energies via isobaric-isothermal flows
We present a machine-learning model based on normalizing flows that is t...
share
research
∙
02/09/2023
Equivariant MuZero
Deep reinforcement learning repeatedly succeeds in closed, well-defined ...
research
∙
09/28/2022
Score Modeling for Simulation-based Inference
Neural Posterior Estimation methods for simulation-based inference can b...
research
∙
09/22/2022
A Generalist Neural Algorithmic Learner
The cornerstone of neural algorithmic reasoning is the ability to solve ...
research
∙
11/16/2021
Normalizing flows for atomic solids
We present a machine-learning approach, based on normalizing flows, for ...
research
∙
06/08/2020
The Lipschitz Constant of Self-Attention
Lipschitz constants of neural networks have been explored in various con...
research
∙
02/12/2020
Targeted free energy estimation via learned mappings
Free energy perturbation (FEP) was proposed by Zwanzig more than six dec...
research
∙
02/10/2020
On Contrastive Learning for Likelihood-free Inference
Likelihood-free methods perform parameter inference in stochastic simula...
research
∙
02/07/2020
Causally Correct Partial Models for Reinforcement Learning
In reinforcement learning, we can learn a model of future observations a...
research
∙
02/06/2020
Normalizing Flows on Tori and Spheres
Normalizing flows are a powerful tool for building expressive distributi...
research
∙
12/05/2019
Normalizing Flows for Probabilistic Modeling and Inference
Normalizing flows provide a general mechanism for defining expressive pr...
research
∙
10/29/2019
Neural Density Estimation and Likelihood-free Inference
I consider two problems in machine learning and statistics: the problem ...
research
∙
06/10/2019
Neural Spline Flows
A normalizing flow models a complex probability density as an invertible...
research
∙
06/05/2019
Cubic-Spline Flows
A normalizing flow models a complex probability density as an invertible...
research
∙
11/21/2018
Sequential Neural Methods for Likelihood-free Inference
Likelihood-free inference refers to inference when a likelihood function...
research
∙
05/18/2018
Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows
We present Sequential Neural Likelihood (SNL), a new method for Bayesian...
research
∙
05/19/2017
Masked Autoregressive Flow for Density Estimation
Autoregressive models are among the best performing neural density estim...
research
∙
05/20/2016
Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation
Many statistical models can be simulated forwards but have intractable l...
research
∙
10/08/2015
