Tengyu Ma    @tengyuma   ·   10/13/2021
Thinking of applying self-supervised learning (SSL) on your uncurated, imbalanced datasets? Good news: we found SSL is more robust to long tails than supervised representations. We also present theoretical and empirical analyses and an improved algorithm.
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Yann LeCun    @ylecun   ·   6/29/2021
A thread on self-supervised learning, with links.
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DeepMind    @DeepMind   ·   6/16/2020
Moving away from negative pairs in self-supervised representation learning: our new SotA method, Bootstrap Your Own Latent (BYOL), narrows the gap between self-supervised & supervised methods simply by predicting previous versions of itself. See here:
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Sergey Levine    @svlevine   ·   10/11/2021
In robotic learning, we start from scratch for every experiment, with custom per-task data. Generalization is poor, because the dataset is narrow. In supervised learning domains, there are large reusable datasets (e.g., ImageNet, MS-COCO). What would be "ImageNet" in robotics?
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Sebastian Raschka    @rasbt   ·   9/25/2021
There is a good section on quantifying annotator uncertainty. Ties in to of what I have been thinking about lately, i.e., how to encode natural labeling ambiguity and quality for supervised learning algos
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Tengyu Ma
Assistant Professor at Stanford, working on machine learning, deep learning, reinforcement learning, representation learning, and their theory.

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