no code implementations • ICLR 2019 • Xiaoyu Lu, Jan Stuehmer, Katja Hofmann
In this paper, we use a generative model to capture different emergent playstyles in an unsupervised manner, enabling the imitation of a diverse range of distinct behaviours.
1 code implementation • 24 Feb 2023 • Ruchika Chavhan, Henry Gouk, Jan Stuehmer, Calum Heggan, Mehrdad Yaghoobi, Timothy Hospedales
Contrastive self-supervised learning methods famously produce high quality transferable representations by learning invariances to different data augmentations.
no code implementations • pproximateinference AABI Symposium 2022 • Marcello Massimo Negri, Vincent Fortuin, Jan Stuehmer
Variational auto-encoders have proven to capture complicated data distributions and useful latent representations, while advances in meta-learning have made it possible to extract prior knowledge from data.
1 code implementation • 12 Oct 2019 • Niklas Stoehr, Emine Yilmaz, Marc Brockschmidt, Jan Stuehmer
While a wide range of interpretable generative procedures for graphs exist, matching observed graph topologies with such procedures and choices for its parameters remains an open problem.