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Found 4 papers, 2 papers with code
On the Optimal Recovery of Graph Signals
Learning a smooth graph signal from partially observed data is a well-studied task in graph-based machine learning.
A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks
Gradient clipping is usually employed to address this issue in the single machine setting, but exploring this technique in the distributed setting is still in its infancy: it remains mysterious whether the gradient clipping scheme can take advantage of multiple machines to enjoy parallel speedup.
Optimal Recovery from Inaccurate Data in Hilbert Spaces: Regularize, but what of the Parameter?
This article fills the gap in both a local scenario and a global scenario.
Learning from Non-Random Data in Hilbert Spaces: An Optimal Recovery Perspective
Then, for any Hilbert space, we show that Optimal Recovery provides a formula which is user-friendly from an algorithmic point-of-view, as long as the hypothesis class is linear.
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