ICML 2019 Meta-Learning Tutorial

Search this site p Meta-Learning: from Few-Shot Learning to Rapid Reinforcement Learning ICML 2019 Tutorial Abstract In recent years, high-capacity models, such as deep neural networks, have enabled very powerful machine learning techniques in domains where data is plentiful. However, domains where data is scarce have proven challenging for such methods because high-capacity function approximators critically rely on large datasets for generalization. This can pose a major challenge for domains ranging from supervised medical image processing to reinforcement learning where real-world data collection (e.g., for robots) poses a major logistical challenge. Meta-learning or few-shot learning offers a potential solution to this problem: by learning to learn across data from many previous tasks, few-shot meta-learning algorithms can discover the structure among tasks to enable fast learning of new tasks. The objective of this tutorial is to provide a unified perspective of meta-learning: te...

8 mentions: @svlevine@learn_learning3@learn_learning3@chelseabfinn@hamadakoichi@ottamm_190@tensorBored@GygliMichael
Date: 2019/06/10 20:17

Referring Tweets

@svlevine We've compiled a meta-reading list for our meta-learning tutorial: https://t.co/3i5zohN4KM Short list of the main papers we covered in our meta-learning tutorial: https://t.co/g3eAcsO0vr https://t.co/TdWZNyn9kB
@learn_learning3 ICML2019Tutorial資料集(1/2) Attention in Deep Learning https://t.co/I8ZqSqZAeu Meta-Learning: from Few-Shot Learning to Rapid Reinforcement Learning https://t.co/mFH5lCUorS Never-Ending Learning https://t.co/ktTVP3u1oT A Primer on PAC-Bayesian Learning https://t.co/NrZk8qpJRR
@learn_learning3 ICML2019のチュートリアル"Meta-Learning: from Few-Shot Learning to Rapid Reinforcement Learning"の資料 https://t.co/mFH5lCUorS なぜメタ学習が必要か?から始まり,入手データの制限を克服していかに汎用モデルを学習するかという視点でCV, NLP, RLにおけるメタ学習の数理,関連研究を解説 https://t.co/KXwpS42btC

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