38 citations · 81 across the 19 of their papers we have counts for
4 papers · 1 filter
Harnessing Attention Mechanisms: Efficient Sequence Reduction using Attention-based Autoencoders
Daniel Biermann, Fabrizio Palumbo, Morten Goodwin +1
Many machine learning models use the manipulation of dimensions as a driving force to enable models to identify and learn important features in data. In the case of sequential data…
State Representation Learning Using an Unbalanced Atlas
Li Meng, Morten Goodwin, Anis Yazidi +1
The manifold hypothesis posits that high-dimensional data often lies on a lower-dimensional manifold and that utilizing this manifold as the target space yields more efficient repr…
Unsupervised Representation Learning in Partially Observable Atari Games
Li Meng, Morten Goodwin, Anis Yazidi +1
State representation learning aims to capture latent factors of an environment. Contrastive methods have performed better than generative models in previous state representation le…
A Contrastive Learning Scheme with Transformer Innate Patches
Sander Riisøen Jyhne, Per-Arne Andersen, Morten Goodwin
This paper presents Contrastive Transformer, a contrastive learning scheme using the Transformer innate patches. Contrastive Transformer enables existing contrastive learning techn…