Feb 10, 2020 · In this work, we show that both of these approaches can be unified under a general contrastive learning scheme, and clarify how they should be ...

In this paper, we consider two recently proposed approaches to neural likelihood-free inference: the first uses classifica- tion to approximate density ratios ...

In this paper, we consider two recently proposed approaches to neural likelihood-free inference: the first uses classifica- tion to approximate density ratios ...

This work shows that two popular likelihood-free approaches to parameter inference in stochastic simulator models can be unified under a general contrastive ...

Jul 13, 2020 · In this work, we show that both of these approaches can be unified under a general contrastive learning scheme, and clarify how they should be ...

On Contrastive Learning for Likelihood-free Inference. Supplementary Material. A. Additional experimental results. A.1. SRE vs SNPE-C. (a) Nonlinear Gaussian. ( ...

Papertalk is an open-source platform where scientists share video presentations about their newest scientific results - and watch, like + discuss them.

Jul 12, 2020 · Likelihood-free methods perform parameter inference in stochastic simulator models where evaluating the likelihood is intractable but sampling ...

Durkan et al., On Contrastive Learning for Likelihood-free Inference, 2020. [arXiv]. Features neural likelihood-free methods from. Papamakarios et al ...

Oct 22, 2024 · Contrastive learning is an intuitive and computationally feasible alternative to likelihood ... Likelihood-free inference via classification. arXiv ...