Search Results for author:
Found 4 papers, 2 papers with code
Trajectory VAE for multi-modal imitation
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.
Amortised Invariance Learning for Contrastive Self-Supervision
Contrastive self-supervised learning methods famously produce high quality transferable representations by learning invariances to different data augmentations.
Meta-learning richer priors for VAEs
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.
Disentangling Interpretable Generative Parameters of Random and Real-World Graphs
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.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
