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Here, we explore a popular exploration heuristic, Thompson sampling, and note implementation details for multi-arm and contextual bandit variants. While the contextual bandit requires greater computational and technical complexity to include contextual features in the decision process, we illustrate the ...
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今天,我们介绍一个真正的贝叶斯方法:汤普森采样(Thompson Sampling)。在我们讨论Upper Confidence Bound或者UCB1的时,我们说过,作选择时只看预期收益并不够准确。所以,UCB1在预期收益的基础上增加了一个“奖励”。这个奖励会随着一个老虎机被选择次数的增加而缩小。在上一篇文章里,我们讨论了Gradient Bandit算法。在其中...
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Tutorial on Diffusion Models for Imaging and Vision by Stanley H. Chan Understanding Deep Learning by Simon J.D. Prince Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David Large Language Models A Visual Guide to Quantization: Demystifying the Comp...
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