Stanford CS330: Multi-Task and Meta-Learning, 2019 by Chelsea Finn. Meta Learning lecture by Soheil Feizi. Chelsea Finn: Building Unsupervised Versatile Agents with Meta-Learning. Sam Ritter: Meta-Learning to Make Smart Inferences from Small Data. Model Agnostic Meta Learning by Siavash Khodadadeh. Meta Learning by Siraj Raval. Meta Learning by Hugo Larochelle
Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning , Anusha Nagabandi, Ignasi Clavera, Simin Liu, Ronald S. Fearing, Pieter Abbeel, Sergey Levine, Chelsea Finn, ICLR 2019. Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL , Anusha Nagabandi, Chelsea Finn, Sergey Levine, ICLR 2019.
Oct 10, 2019 · Background Online Meta-Learning Problem Algorithm Experiments Conclusion Online Meta-Learning Chelsea Finn*, Aravind Rajeswran*, Sham Kakade, Sergey Levine University of California, Berkeley ICML 2019 October 10, 2019 1/32
Jun 12, 2020 · Multi-task and meta-learning seem to have found a sweet spot in deep reinforcement learning applications such as robotics and games. Well-known researchers, who are pioneering multi-task and meta-learning, include Professor Sergey Levine and Professor Pieter Abbeel from UC Berkeley, and Professor Chelsea Finn from Stanford University.
variable and learn it using backpropagation. Run run maml(n way=5, k shot=1, inner update lr=0.4, num inner updates=1, learn inner update lr=True). Also try with inner update lr being 0:04 and 4:0. Submit a plot of the meta-validation accuracy over meta-training iterations and state how it compares to the MAML with xed inner update lr.
Mar 27, 2019 · A Meta-Learning Approach for Custom Model Training by Amir Erfan Eshratifar et al. AAA I2019. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks by Chelsea Finn NeurIPS 2017. On First-Order Meta-Learning Algorithms by Alex Nichol 2018.
Chelsea Finn, Pieter Abbeel, Sergey Levine. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. ICML 2017. Alex Nichol, Joshua Achiam, John Schulman. On First-Order Meta-Learning Algorithms. arXiv 2018. Sebastian Flennerhag, Andrei A. Rusu, Razvan Pascanu, Francesco Visin, Hujun Yin, Raia Hadsell.
Dec 13, 2019 · Invited Talk: The Big Problem with Meta-Learning and How Bayesians Can Fix It by. Chelsea Finn · Dec 13, 2019 · ...
Model-Agnostic Meta-Learning (MAML) was introduced in 2017 by Chelsea Finn et al. Given a sequence of tasks, the parameters of a given model are trained such that few iterations of gradient descent with few training data from a new task will lead to good generalization performance on that task. MAML "trains the model to be easy to fine-tune."
[28] Kyle Hsu, Sergey Levine, Chelsea Finn. Unsupervised Learning via Meta-Learning. International Conference on Learning Representations (ICLR). Chelsea Finn, Lisa Anne Hendricks, Trevor Darrell Learning Compact Convolutional Neural Networks with Nested Dropout.
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  • Узнать причину. Закрыть. Meta-Learning Symmetries and Distributions - Chelsea Finn. Machine learning - Bayesian optimization and multi-armed bandits - Продолжительность: 1:20:30 Nando de Freitas 81 304 просмотра.
  • Oct 10, 2019 · Background Online Meta-Learning Problem Algorithm Experiments Conclusion Online Meta-Learning Chelsea Finn*, Aravind Rajeswran*, Sham Kakade, Sergey Levine University of California, Berkeley ICML 2019 October 10, 2019 1/32

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Meta-Learning challenges. Following the success of the AutoDL 2019-2020 challenge series (which was part of the competition selection of NeurIPS 2019), we are starting to organize a series of challenges on Meta-Learning. We co-schedule a workshop on Meta-Learning at AAAI, Februa2021 in Vancouver, Canada. We are happy to announce Chelsea Finn (Stanford University), Oriol Vinyals (Google Deepmind), Lilian Weng (OpenAI) and Richard Zemel (University of Toronto) as our keynote speakers.

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Скачать CHELSEA FINN, смотреть онлайн CHELSEA FINN в HD качестве. Chelsea Finn Building Unsupervised Versatile Agents With Meta-learning. Allen Institute for AI 2018-10-27 - 20:11:25. How Robots Learn In Conversation With Chelsea Finn.

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Get this from a library! Learning to Learn with Gradients. [Chelsea Finn] -- Humans have a remarkable ability to learn new concepts from only a few examples and quickly adapt to unforeseen circumstances. To do so, they build upon their prior experience and prepare for the ...

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Meta learning is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments. As of 2017 the term had not found a standard interpretation...


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Meta-Learning challenges. Following the success of the AutoDL 2019-2020 challenge series (which was part of the competition selection of NeurIPS 2019), we are starting to organize a series of challenges on Meta-Learning. We co-schedule a workshop on Meta-Learning at AAAI, Februa2021 in Vancouver, Canada. We are happy to announce Chelsea Finn (Stanford University), Oriol Vinyals (Google Deepmind), Lilian Weng (OpenAI) and Richard Zemel (University of Toronto) as our keynote speakers.

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Machine learning systems are often designed under the assumption that they will be deployed as a static model in a single static region of the world. In this talk, I'll discuss how we can allow deep networks to be robust to such distribution shift via adaptation. I will focus on meta-learning algorithms...

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Key functionality for the supervised learning part of Model-Agnostic Meta-Learning (Finn et al 2017) - maml.py

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研究内容这篇发表于EMNLP 2018的文章提出了MetaNMT。这篇文章研究的是神经机器翻译(NMT)领域里的少样本学习任务。作者通过将不同的language pair当作不同的任务,使用MAML算法[1]获取来获取较好的初始化模型参数,使得在少样本的新language pair任务中能够快速学习;此外,作者认为在NMT领域中,不同 ...

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Aug 08, 2018 · To study the problem of learning to learn, we first develop a clear and formal definition of the meta-learning problem, its terminology, and desirable properties of meta-learning algorithms. Building upon these foundations, we present a class of model-agnostic meta-learning methods that embed gradient-based optimization into the learner.

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Chelsea Finn Stanford While meta-learning has shown tremendous potential for enabling earning and generalization from only a few examples, its success beyond few-shot learning has remained less clear.

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Pierre-Luc Bacon · Marc Deisenroth · Chelsea Finn · Erin Grant · Thomas L Griffiths · Abhishek Gupta · Nicolas Heess · Michael L. Littman · Junhyuk Oh. Poster. Tue May 07 09:00 AM -- 11:00 AM (PDT) @ Great Hall BC #28. Meta-Learning with Latent Embedding Optimization.

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Jan 31, 2018 · Chelsea is a PhD candidate in computer science at UC Berkeley and she’s interested in how learning algorithms can enable robots to acquire common sense, allowing them to learn a variety of complex sensory motor skills in real-world settings. She completed her bachelor’s degree at MIT and has also spent time at Google Brain.

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研究内容这篇发表于EMNLP 2018的文章提出了MetaNMT。这篇文章研究的是神经机器翻译(NMT)领域里的少样本学习任务。作者通过将不同的language pair当作不同的任务,使用MAML算法[1]获取来获取较好的初始化模型参数,使得在少样本的新language pair任务中能够快速学习;此外,作者认为在NMT领域中,不同 ...

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Chelsea Finn, Pieter Abbeel, Sergey Levine. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. ICML 2017. Alex Nichol, Joshua Achiam, John Schulman. On First-Order Meta-Learning Algorithms. arXiv 2018. Sebastian Flennerhag, Andrei A. Rusu, Razvan Pascanu, Francesco Visin, Hujun Yin, Raia Hadsell.

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深入浅出Meta Learning - 让机器学会如何去学习. 在2017年初,Chelsea Finn等人提出了 MAML: ModelAgnostic Meta Learning。

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May 29, 2020 · Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In Proceedings of the 34th International Conference on Machine LearningVolume 70, pp. 1126–1135. JMLR. org, 2017; Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. Deepwalk: Online learning of social representations.

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We refer the reader to the excellent blog post "Learning to Learn" [7] by Chelsea Finn for a more complete survey of the meta-learning literature. For the remainder of this section we focus on the papers that introduce the methods we make use of in our work. Specifically, these papers are "One-Shot Visual Imitation Learning via Meta-Learning" [1],

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meta-learning be used to continuously train and improve the performance of deep neural ... Chelsea Finn, Pieter Abbeel, and Sergey Levine. "Model-Agnostic

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Listen to Chelsea Finn | SoundCloud is an audio platform that lets you listen to what you love and share the sounds you create.. Stream Tracks and Playlists from Chelsea Finn on your desktop or mobile device.

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for Visual Meta-Reinforcement Learning Allan Jabri Kyle Hsu,yBenjamin Eysenbach Abhishek Gupta Sergey Levine Chelsea Finn Abstract In principle, meta-reinforcement learning algorithms leverage experience across many tasks to learn fast reinforcement learning (RL) strategies that transfer to similar tasks.

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The Peculiar Optimization and Regularization Challenges...Meta-Learning - Chelsea Finn Video of The Peculiar Optimization and Regularization Challenges...Meta-Learning - Chelsea Finn Search form

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A curated list of Meta Learning papers, code, books, blogs, videos, datasets and other resources. Meta-Learning Deep Visual Words for Fast Video Object Segmentation, (2019), Harkirat Singh Behl, Mohammad Najafi, Anurag Arnab, Philip H.S. Torr. [pdf].

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[論文解説] MAML: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks - Qiita 以下の論文の解説(まとめ)になります. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks この論文は,Chelsea Finnが出した論文でICML...

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Mar 14, 2018 · Bio: Chelsea Finn is a PhD candidate in Computer Science at UC Berkeley, studying machine learning for perception and control of embodied systems. She is interested in how learning algorithms can enable machines to acquire common sense, allowing them to learn a variety of complex sensorimotor skills in real-world settings.

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[3] Chelsea Finn, Pieter Abbeel, and Sergey Levine. "Model-agnostic meta- learning for fast adaptation of deep networks" [4] Antreas Antoniou, Harrison Edwards, and Amos Storkey. "How to train your MAML" Student Progress on MNIST Task After 2k Meta- tep NO Teacher klaptive Teacher Student Progress on Omniglot 5 Way Task After 1k Meta-Steps

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Meta-learning, also known as "learning to learn", intends to design models that can learn new skills or adapt to new environments rapidly with a few training examples. There are three common approaches: 1) learn an efficient distance metric (metric-based); 2) use (recurrent)...

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Annie Xie, Avi Singh, Sergey Levine, Chelsea Finn Conference on Robot Learning (CoRL), 2018 One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning Tianhe Yu, Chelsea Finn, Annie Xie, Sudeep Dasari, Tianhao Zhang, Pieter Abbeel, Sergey Levine

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Chelsea Finn is an Assistant Professor in Computer Science and Electrical Engineering at Stanford University. Professor Finn's research interests lie in the ability to enable robots and other agents to develop broadly intelligent behavior through learning and interaction.

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Chelsea Finn, Pieter Abbeel, and Sergey Levine. “Model-agnostic meta-learning for fast adaptation of deep networks.” ICML, 2017. Finn, Chelsea, and Sergey Levine. “Meta-learning and universality: Deep representations and gradient descent can approximate any learning algorithm.” arXiv preprint arXiv:1710.11622 (2017).

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A continual learning survey: Defying forgetting in classification tasks: Matthias De Lange et al. An Introduction to Continual Learning: break 06/11/20: Janith: Meta-Learning Symmetries by Reparameterization: Allan Zhou, Tom Knowles & Chelsea Finn 13/11/20: Daniel: GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability

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Meta-Learning challenges. Following the success of the AutoDL 2019-2020 challenge series (which was part of the competition selection of NeurIPS 2019), we are starting to organize a series of challenges on Meta-Learning. We co-schedule a workshop on Meta-Learning at AAAI, Februa2021 in Vancouver, Canada. We are happy to announce Chelsea Finn (Stanford University), Oriol Vinyals (Google Deepmind), Lilian Weng (OpenAI) and Richard Zemel (University of Toronto) as our keynote speakers.

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Dec 12, 2019 · Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables, by Kate Rakelly, Aurick Zhou, Deirdre Quillen, Chelsea Finn, Sergey Levine Original Abstract. Deep reinforcement learning algorithms require large amounts of experience to learn an individual task.

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Assistant Professor Chelsea Finn, Stanford University http://cs330.stanford.edu/ To get the latest news on Stanford’s upcoming professional programs in Artif...

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Aravind Rajeswaran, Chelsea Finn, Sham M. Kakade, Sergey Levine. Abstract <p>A core capability of intelligent systems is the ability to quickly learn new tasks by drawing on prior experience. Gradient (or optimization) based meta-learning has recently emerged as an effective approach for few-shot learning.
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In this course, you will learn the foundational principles that drive these applications and practice implementing some of these systems. Specific topics include machine learning, search, game playing, Markov decision processes, constraint satisfaction, graphical models, and logic.


In principle, meta-reinforcement learning algorithms leverage experience across many tasks to learn fast and effective reinforcement learning (RL) strategies. However, current meta-RL approaches rely on manually-defined distributions of training tasks, and hand-crafting these task distributions can be challenging and timeconsuming.