Search Results for author: Max Sobol Mark

Found 2 papers, 2 papers with code

Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning

1 code implementation9 Mar 2023 Mitsuhiko Nakamoto, Yuexiang Zhai, Anikait Singh, Max Sobol Mark, Yi Ma, Chelsea Finn, Aviral Kumar, Sergey Levine

Our approach, calibrated Q-learning (Cal-QL) accomplishes this by learning a conservative value function initialization that underestimates the value of the learned policy from offline data, while also being calibrated, in the sense that the learned Q-values are at a reasonable scale.

Offline RL Q-Learning +1

Unsupervised Learning from Video with Deep Neural Embeddings

1 code implementation CVPR 2020 Chengxu Zhuang, Tianwei She, Alex Andonian, Max Sobol Mark, Daniel Yamins

Because of the rich dynamical structure of videos and their ubiquity in everyday life, it is a natural idea that video data could serve as a powerful unsupervised learning signal for training visual representations in deep neural networks.

Action Recognition Object Recognition

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