345 citations · 1.1k across the 20 of their papers we have counts for
12 papers · 1 filter
The Lipschitz Constant of Self-Attention
Hyunjik Kim, George Papamakarios, Andriy Mnih
Lipschitz constants of neural networks have been explored in various contexts in deep learning, such as provable adversarial robustness, estimating Wasserstein distance, stabilisin…
On Contrastive Learning for Likelihood-free Inference
Conor Durkan, Iain Murray, George Papamakarios
Likelihood-free methods perform parameter inference in stochastic simulator models where evaluating the likelihood is intractable but sampling synthetic data is possible. One class…
Normalizing Flows on Tori and Spheres
Danilo Jimenez Rezende, George Papamakarios, Sébastien Racanière +4
Normalizing flows are a powerful tool for building expressive distributions in high dimensions. So far, most of the literature has concentrated on learning flows on Euclidean space…
Normalizing Flows for Probabilistic Modeling and Inference
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende +2
Normalizing flows provide a general mechanism for defining expressive probability distributions, only requiring the specification of a (usually simple) base distribution and a seri…
Neural Density Estimation and Likelihood-free Inference
George Papamakarios
I consider two problems in machine learning and statistics: the problem of estimating the joint probability density of a collection of random variables, known as density estimation…
Cubic-Spline Flows
Conor Durkan, Artur Bekasov, Iain Murray +1
A normalizing flow models a complex probability density as an invertible transformation of a simple density. The invertibility means that we can evaluate densities and generate sam…
Neural Spline Flows
Conor Durkan, Artur Bekasov, Iain Murray +1
A normalizing flow models a complex probability density as an invertible transformation of a simple base density. Flows based on either coupling or autoregressive transforms both o…
Sequential Neural Methods for Likelihood-free Inference
Conor Durkan, George Papamakarios, Iain Murray
Likelihood-free inference refers to inference when a likelihood function cannot be explicitly evaluated, which is often the case for models based on simulators. Most of the literat…
Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows
George Papamakarios, David C. Sterratt, Iain Murray
We present Sequential Neural Likelihood (SNL), a new method for Bayesian inference in simulator models, where the likelihood is intractable but simulating data from the model is po…
Masked Autoregressive Flow for Density Estimation
George Papamakarios, Theo Pavlakou, Iain Murray
Autoregressive models are among the best performing neural density estimators. We describe an approach for increasing the flexibility of an autoregressive model, based on modelling…
Fast -free Inference of Simulation Models with Bayesian Conditional Density Estimation
George Papamakarios, Iain Murray
Many statistical models can be simulated forwards but have intractable likelihoods. Approximate Bayesian Computation (ABC) methods are used to infer properties of these models from…
Distilling Model Knowledge
George Papamakarios
Top-performing machine learning systems, such as deep neural networks, large ensembles and complex probabilistic graphical models, can be expensive to store, slow to evaluate and h…