
The Lipschitz Constant of SelfAttention
Lipschitz constants of neural networks have been explored in various con...
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Targeted free energy estimation via learned mappings
Free energy perturbation (FEP) was proposed by Zwanzig more than six dec...
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On Contrastive Learning for Likelihoodfree Inference
Likelihoodfree methods perform parameter inference in stochastic simula...
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Causally Correct Partial Models for Reinforcement Learning
In reinforcement learning, we can learn a model of future observations a...
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Normalizing Flows on Tori and Spheres
Normalizing flows are a powerful tool for building expressive distributi...
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Normalizing Flows for Probabilistic Modeling and Inference
Normalizing flows provide a general mechanism for defining expressive pr...
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Neural Density Estimation and Likelihoodfree Inference
I consider two problems in machine learning and statistics: the problem ...
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Neural Spline Flows
A normalizing flow models a complex probability density as an invertible...
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CubicSpline Flows
A normalizing flow models a complex probability density as an invertible...
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Sequential Neural Methods for Likelihoodfree Inference
Likelihoodfree inference refers to inference when a likelihood function...
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Sequential Neural Likelihood: Fast Likelihoodfree Inference with Autoregressive Flows
We present Sequential Neural Likelihood (SNL), a new method for Bayesian...
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Masked Autoregressive Flow for Density Estimation
Autoregressive models are among the best performing neural density estim...
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Fast εfree Inference of Simulation Models with Bayesian Conditional Density Estimation
Many statistical models can be simulated forwards but have intractable l...
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Distilling Model Knowledge
Topperforming machine learning systems, such as deep neural networks, l...
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George Papamakarios
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