2023 - Research.com Rising Star of Science Award
2022 - Research.com Rising Star of Science Award
Her main research concerns Artificial intelligence, Machine learning, Reinforcement learning, Robot and Meta learning. When carried out as part of a general Artificial intelligence research project, her work on Control and Latent variable is frequently linked to work in Video prediction, Key and Equivalence, therefore connecting diverse disciplines of study. In the field of Machine learning, her study on Gradient descent overlaps with subjects such as Sample.
Her Gradient descent research is multidisciplinary, incorporating perspectives in Contextual image classification and Training set. Her Reinforcement learning research is multidisciplinary, incorporating elements of Artificial neural network, Supervised learning and Convolutional neural network. She has researched Robot in several fields, including Object, Pixel, Computer vision, Human–computer interaction and Range.
Chelsea Finn focuses on Artificial intelligence, Reinforcement learning, Machine learning, Robot and Human–computer interaction. Her studies in Artificial intelligence integrate themes in fields like Adaptation and Computer vision. Her research investigates the connection between Reinforcement learning and topics such as Artificial neural network that intersect with problems in Stability.
Her Leverage study, which is part of a larger body of work in Machine learning, is frequently linked to Space, bridging the gap between disciplines. Her Robot study combines topics in areas such as Object and Representation. Her research investigates the connection between Human–computer interaction and topics such as Variety that intersect with issues in Pixel.
Chelsea Finn spends much of her time researching Reinforcement learning, Artificial intelligence, Machine learning, Robot and Human–computer interaction. Her study in Reinforcement learning is interdisciplinary in nature, drawing from both Control, Leverage and Set. Her work in the fields of Deep learning and Image overlaps with other areas such as Meta learning and Generalization.
Her Machine learning research includes themes of Variety, Training set and Robustness. Chelsea Finn has included themes like Task and Adaptation in her Robot study. Her work deals with themes such as Object and Statistical model, which intersect with Human–computer interaction.
Chelsea Finn spends much of her time researching Artificial intelligence, Reinforcement learning, Machine learning, Robot and Human–computer interaction. Her work in the fields of Artificial intelligence, such as Deep learning and Contextual image classification, intersects with other areas such as Code and Meta learning. Her study looks at the relationship between Reinforcement learning and topics such as Image, which overlap with Range.
Her biological study spans a wide range of topics, including Control and Training set. Her Robot research includes elements of Modality, Adaptation and Computer vision. Her Human–computer interaction research is multidisciplinary, incorporating perspectives in Transfer of learning and Statistical model.
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn;Pieter Abbeel;Sergey Levine.
international conference on machine learning (2017)
End-to-end training of deep visuomotor policies
Sergey Levine;Chelsea Finn;Trevor Darrell;Pieter Abbeel.
Journal of Machine Learning Research (2016)
Unsupervised Learning for Physical Interaction through Video Prediction
Chelsea Finn;Ian J. Goodfellow;Sergey Levine.
neural information processing systems (2016)
Guided cost learning: deep inverse optimal control via policy optimization
Chelsea Finn;Sergey Levine;Pieter Abbeel.
international conference on machine learning (2016)
Deep visual foresight for planning robot motion
Chelsea Finn;Sergey Levine.
international conference on robotics and automation (2017)
Probabilistic Model-Agnostic Meta-Learning
Chelsea Finn;Kelvin Xu;Sergey Levine.
neural information processing systems (2018)
Stochastic Adversarial Video Prediction
Alex X. Lee;Richard Zhang;Frederik Ebert;Pieter Abbeel.
arXiv: Computer Vision and Pattern Recognition (2018)
Model Based Reinforcement Learning for Atari
Łukasz Kaiser;Mohammad Babaeizadeh;Piotr Miłos;Błażej Osiński.
international conference on learning representations (2020)
Model-Based Reinforcement Learning for Atari
Lukasz Kaiser;Mohammad Babaeizadeh;Piotr Milos;Blazej Osinski.
arXiv: Learning (2019)
Deep spatial autoencoders for visuomotor learning
Chelsea Finn;Xin Yu Tan;Yan Duan;Trevor Darrell.
international conference on robotics and automation (2016)
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