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During this quarantine time, I binge-watched @Stanford #CS330 lectures taught by the brilliant @chelseabfinn. This blog post is a summary of the key takeaways on #Bayesian Meta-Learning that I’ve learned. #AtHomeWithAI
medium.com/cracking-the-d…
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During this quarantine time, I binge-watched @Stanford #CS330 lectures taught by the brilliant @chelseabfinn. This blog post is a summary of the key takeaways on #Bayesian Meta-Learning that I’ve learned. #AtHomeWithAI
medium.com/cracking-the-d…
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Bayesian meta-learning generates hypotheses about the underlying function, samples from the data distribution, and reasons about model uncertainty. It is suitable for problems in safety-critical domains, exploration strategies for meta-RL, and active learning.
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There are various Bayesian Meta-Learners pre-neural-nets:
- @drfeifei, @rob_fergus, and @PietroPerona: vision.stanford.edu/documents/Fei-…
- @rsalakhu, @wise_tbaum, and Torralba: proceedings.mlr.press/v27/salakhutdi…
- @LakeBrenden, @rsalakhu, and @wise_tbaum: papers.nips.cc/paper/5128-one…
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- @drfeifei, @rob_fergus, and @PietroPerona: vision.stanford.edu/documents/Fei-…
- @rsalakhu, @wise_tbaum, and Torralba: proceedings.mlr.press/v27/salakhutdi…
- @LakeBrenden, @rsalakhu, and @wise_tbaum: papers.nips.cc/paper/5128-one…
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