284 citations · 1.6k across the 20 of their papers we have counts for
8 papers · 1 filter
Constellation: Learning relational abstractions over objects for compositional imagination
James C. R. Whittington, Rishabh Kabra, Loic Matthey +2
Learning structured representations of visual scenes is currently a major bottleneck to bridging perception with reasoning. While there has been exciting progress with slot-based m…
Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents
Jane X. Wang, Michael King, Nicolas Porcel +14
There has been rapidly growing interest in meta-learning as a method for increasing the flexibility and sample efficiency of reinforcement learning. One problem in this area of res…
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…
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…
Towards a Definition of Disentangled Representations
Irina Higgins, David Amos, David Pfau +4
How can intelligent agents solve a diverse set of tasks in a data-efficient manner? The disentangled representation learning approach posits that such an agent would benefit from s…
Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies
Alessandro Achille, Tom Eccles, Loic Matthey +4
Intelligent behaviour in the real-world requires the ability to acquire new knowledge from an ongoing sequence of experiences while preserving and reusing past knowledge. We propos…