Semanlink - Google Brain AND Machine learning

Durk Kingma sur Twitter : about likelihood-based generative models

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2018-12-07 About

Time-Contrastive Networks: Self-Supervised Learning from Video (2017)

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Self-supervised approach for learning representations and robotic behaviors entirely from unlabeled videos recorded from multiple viewpoints, and study how this representation can be used in two robotic imitation settings: imitating object interactions from videos of humans, and imitating human poses.

> We train our representations using a metric learning loss, where multiple simultaneous viewpoints of the same observation are attracted in the embedding space, while being repelled from temporal neighbors which are often visually similar but functionally different. In other words, the model simultaneously learns to recognize what is common between different-looking images, and what is different between similar-looking images.
> This signal causes our model to discover attributes that do not change across viewpoint, but do change across time, while ignoring nuisance variables such as occlusions, motion blur, lighting and background. We demonstrate that this representation can be used by a robot to directly mimic human poses without an explicit correspondence, and that it can be used as a reward function within a reinforcement learning algorithm.

2018-10-27 About

[1703.03129] Learning to Remember Rare Events

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2018-10-23 About

Machine Learning for Systems and Systems for Machine Learning (NIPS 2017)

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2017-12-12 About

Google's neural networks invent their own encryption | New Scientist

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2016-11-06 About