On Learning Invariant Representations for Domain Adaptation – Blog | Machine Learning | Carnegie Mellon University

On Learning Invariant Representations for Domain Adaptation – Blog | Machine Learning | Carnegie Mellon University

One of the backbone assumptions underpinning the generalization theory of supervised learning algorithms is that the test distribution should be the same as the training distribution. However in many real-world applications it is usually time-consuming or even infeasible to collect labeled data from

7 mentions: @mtoneva1@mldcmu@towards_AI@DAIBuilds
Date: 2019/09/13 16:46

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@mtoneva1 New ML@CMU blog post about learning invariant representations for domain adaptation, written by @HanZhao_Keira and edited by Liam Li! t.co/T3VfImsrRG

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