research
∙
05/30/2023
Exploring the Promise and Limits of Real-Time Recurrent Learning
Real-time recurrent learning (RTRL) for sequence-processing recurrent ne...
share
research
∙
05/24/2023
Contrastive Training of Complex-Valued Autoencoders for Object Discovery
Current state-of-the-art object-centric models use slots and attention-b...
research
∙
03/25/2022
Unsupervised Learning of Temporal Abstractions with Slot-based Transformers
The discovery of reusable sub-routines simplifies decision-making and pl...
research
∙
11/25/2020
Unsupervised Object Keypoint Learning using Local Spatial Predictability
We propose PermaKey, a novel approach to representation learning based o...
research
∙
09/09/2018