Jun 15, 2019 · Meta-Learning: Challenges and Frontiers by. Chelsea Finn · Jun 15, 2019 · ... Chelsea Finn (@chelseabfinn). 218 posts 28055 followers 278 followings. CS Faculty @Stanford. Want your robot to explore intelligently? We study how to learn to explore & introduce a *efficient* meta-learning method that can lead to optimal exploration.Mar 01, 2019 · The approach that lead authors Finn and Rajeswaran pursue is to combine two different approaches that the teams have explored extensively in recent years: meta-learning and online learning. 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 During this quarantine time, I started watching lectures on Stanford’s CS 330 class on Deep Multi-Task and Meta-Learning taught by the brilliant Chelsea Finn. As a courtesy of her talks, this ...
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks PMLR 2017 Chelsea Finn, Pieter Abbeel, Sergey Levine Berkeley, OpenAI. Introduction •Application Xingyou Song , Yuxiang Yang , Krzysztof Choromanski, Ken Caulwerts, Wenbo Gao, Chelsea Finn, Jie Tan. \Rapidly Adaptable Legged Robots via Evolutionary Meta-Learning ... Rapidly Adaptable Legged Robots via Evolutionary Meta-Learning Xingyou Song*, Yuxiang Yang*, Krzysztof Choromanski, Ken Caluwaerts, Wenbo Gao, Chelsea Finn, Jie Tan International Conference on Intelligent Robots and Systems (IROS) 2020 (Under Review), 2020 Learning to learn is a powerful paradigm for enabling models to learn from data more effectively and efficiently. A popular approach to meta-learning is to train a recurrent model to read in a training dataset as input and output the parameters of a learned model, or output predictions for new test inputs.
Our third guest in the Industrial AI series is Chelsea Finn, PhD student at UC Berkeley. Despite being early in her career, Chelsea is an accomplished researcher with more than 14 published papers in the past 2 years, on subjects like Deep Visual Foresight , Model-Agnostic Meta-Learning and Visuomotor...Chelsea Finn (@chelseabfinn). 218 posts 28055 followers 278 followings. CS Faculty @Stanford. Want your robot to explore intelligently? We study how to learn to explore & introduce a *efficient* meta-learning method that can lead to optimal exploration.Authors:Chelsea Finn, Pieter Abbeel, Sergey Levine. Abstract: We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification...Aug 23, 2019 · Model-Agnostic Meta-Learning(MAML) has been growing more and more popular in the field of meta-learning since it’s first introduced by Finn et al. in 2017. It is a simple, general, and effective… Aug 12, 2018 · Our approach is to combine meta-learning with imitation learning to enable one-shot imitation learning. The core idea is that provided a single demonstration of a particular task, i.e. maneuvering a certain object, the robot can quickly identify what the task is and successfully solve it under different circumstances.
In Part 3 of TWIML's Industrial AI series, Sam Charrington digs into robotics and reinforcement learning with Berkeley PhD student, Chelsea Finn. This talk gets into some of the technical weeds of cutting-edge robotics technologies, including inverse reinforcement learning, meta learning and the benefits and challenges of training robots in ... I would think of meta-learning as broad class of algorithms where feedback is explicitly considered as part of the training data. This is useful for few-shot classification but may not be necessary. I have only seen the meta-learning work by Chelsea Finn, to be honest. "Meta-learning is not for that...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 Chelsea Finn (@chelseabfinn). 218 posts 28055 followers 278 followings. CS Faculty @Stanford. Want your robot to explore intelligently? We study how to learn to explore & introduce a *efficient* meta-learning method that can lead to optimal exploration.
Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift Marvin Zhang ⇤ 1, Henrik Marklund 2, Nikita Dhawan Abhishek Gupta , Sergey Levine1, Chelsea Finn2 1 UC Berkeley, 2 Stanford University Abstract A fundamental assumption of most machine learning algorithms is that the training . Deep Learning запись закреплена. 17 июн в 0:35.
Nov 01, 2020 · Congratulations to Professor Chelsea Finn. She has been awarded an inaugural Samsung AI Researcher of the Year award. Presented at Samsung AI Forum 2020, the five recipients are AI researchers from around the world.At the event, Chelsea's lecture was titled, "From Few-Shot Adaptation to Uncovering Symmetries".
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). 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. Deep Online Learning Via Meta-Learning: Continual Adaptation for Model-Based RL. Anusha Nagabandi, Chelsea Finn, and Sergey Levine (ICLR 2019)
Meta-learning systems can be trained to achieve a large number of tasks and are then tested for their ability to learn new tasks. A famous example of this kind of meta-learning is the so-called Transfer Learning discussed in the Chapter on Advanced CNNs, where networks can successfully learn new image-based tasks from relatively small datasets. Meta-Learning without Memorization. '19. Can we do something about it? If tasks mutually exclusive: single function cannot solve all tasks (i.e. due to label shuing, hiding information). Task-Agnostic Meta-Learning for Few-Shot Learning. CVPR '19 Yin, Tucker, Yuan, Levine, Finn.
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As the title of this post suggests, learning to learn is defined as the concept of meta-learning. This new concept was originally introduced by a paper called Model-Agnostic Meta-Learning for fast adaptation of Deep Networks, a paper co-authored by Chelsea Finn, Peter Abbeel and Sergey Levine at University of Berkeley. Nov 21, 2020 · %0 Conference Paper %T Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning %A Tianhe Yu %A Deirdre Quillen %A Zhanpeng He %A Ryan Julian %A Karol Hausman %A Chelsea Finn %A Sergey Levine %B Proceedings of the Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2020 %E Leslie Pack Kaelbling %E Danica Kragic %E Komei Sugiura %F pmlr-v100 ...
Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift Marvin Zhang ⇤ 1, Henrik Marklund 2, Nikita Dhawan Abhishek Gupta , Sergey Levine1, Chelsea Finn2 1 UC Berkeley, 2 Stanford University Abstract A fundamental assumption of most machine learning algorithms is that the training
Presentation slides on meta-learning methods, with an emphasis on methods that are applicable to 7. Previous Deep Meta-Learning Methods RNNs as learners12 (assuming a suﬃciently expressive  Chelsea Finn, Pieter Abbeel, and Sergey Levine. "Model-Agnostic Meta-Learning for Fast...Dec 05, 2020 · A criminally compelling web site by professional crime writers and crime fighters.
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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.
Nov 13, 2019 · Unsupervised Meta-learning for RL, Gupta et al. 2018 Meta-Reinforcement Learning of Structured Exploration Strategies, Gupta et al. 2018 Watch, Try, Learn, Meta-Learning from Demonstrations and Reward, Zhou et al. 2019 Online Meta-Learning, Finn et al. 2019 Slides and Figures Some slides adapted from Meta-Learning Tutorial at ICML 2019, Finn ...