most citedData augmentation in Bayesian neural networks and the cold posterior effect

5 citations · 7 across the 3 of their papers we have counts for

collaborators

3 papers

stat.ML2021★ 2 cited

InfoNCE is variational inference in a recognition parameterised model

Laurence Aitchison, Stoil Ganev

Here, we show that the InfoNCE objective is equivalent to the ELBO in a new class of probabilistic generative model, the recognition parameterised model (RPM). When we learn the op…

stat.ML2021★ 5 cited

Data augmentation in Bayesian neural networks and the cold posterior effect

Seth Nabarro, Stoil Ganev, Adrià Garriga-Alonso +3

Bayesian neural networks that incorporate data augmentation implicitly use a ``randomly perturbed log-likelihood [which] does not have a clean interpretation as a valid likelihood…

stat.ML2020

Semi-supervised learning objectives as log-likelihoods in a generative model of data curation

Stoil Ganev, Laurence Aitchison

We currently do not have an understanding of semi-supervised learning (SSL) objectives such as pseudo-labelling and entropy minimization as log-likelihoods, which precludes the dev…