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Nick Watters

2 accepted papers

2020

Unsupervised Model Selection for Variational Disentangled Representation Learning

ICLR 2020poster

Disentangled representations have recently been shown to improve fairness, data efficiency and generalisation in simple supervised and reinforcement learning tasks. To extend the benefits of disentangled representations to more complex domains and practical applications, it is important to enable hy…

Cited by 92SourceScholar
2019

Multi-Object Representation Learning with Iterative Variational Inference

ICML 2019oral

Human perception is structured around objects which form the basis for our higher-level cognition and impressive systematic generalization abilities. Yet most work on representation learning focuses on feature learning without even considering multiple objects, or treats segmentation as an (often su…