Statistical Learning
Variational inference via Wasserstein gradient flows
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Below, we proceed to describe our contributions in greater detail. Our framework builds upon the seminal work of Jordan et al. [1998], which introduced the celebrated
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8989e07fc124e7a9bcbdebcc8ace2bc0-Paper.pdf
Neural Information Processing Systems
Meta-learning improvesgeneralization ofmachine learning models when faced with previously unseen tasks by leveraging experiences from different, yet related prior tasks.
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