284 citations · 1.6k across the 20 of their papers we have counts for
5 papers · 1 filter
Unsupervised Model Selection for Variational Disentangled Representation Learning
Sunny Duan, Loic Matthey, Andre Saraiva +4
Disentangled representations have recently been shown to improve fairness, data efficiency and generalisation in simple supervised and reinforcement learning tasks. To extend the b…
COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration
Nicholas Watters, Loic Matthey, Matko Bosnjak +2
Data efficiency and robustness to task-irrelevant perturbations are long-standing challenges for deep reinforcement learning algorithms. Here we introduce a modular approach to add…
Multi-Object Representation Learning with Iterative Variational Inference
Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra +6
Human perception is structured around objects which form the basis for our higher-level cognition and impressive systematic generalization abilities. Yet most work on representatio…
MONet: Unsupervised Scene Decomposition and Representation
Christopher P. Burgess, Loic Matthey, Nicholas Watters +4
The ability to decompose scenes in terms of abstract building blocks is crucial for general intelligence. Where those basic building blocks share meaningful properties, interaction…
Spatial Broadcast Decoder: A Simple Architecture for Learning Disentangled Representations in VAEs
Nicholas Watters, Loic Matthey, Christopher P. Burgess +1
We present a simple neural rendering architecture that helps variational autoencoders (VAEs) learn disentangled representations. Instead of the deconvolutional network typically us…