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Found 2 papers, 1 papers with code
Towards Context-Aware Domain Generalization: Understanding the Benefits and Limits of Marginal Transfer Learning
In this work, we analyze the conditions under which information about the context of an input $X$ can improve the predictions of deep learning models in new domains.
On the Convergence Rate of Gaussianization with Random Rotations
Gaussianization is a simple generative model that can be trained without backpropagation.
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