8 citations · 20 across the 7 of their papers we have counts for
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cs.LG2019
Improving Supervised Phase Identification Through the Theory of Information Losses
Brandon Foggo, Nanpeng Yu
This paper considers the problem of Phase Identification in power distribution systems. In particular, it focuses on improving supervised learning accuracies by focusing on exploit…
cs.LG2019★ 1 cited
Analyzing Data Selection Techniques with Tools from the Theory of Information Losses
Brandon Foggo, Nanpeng Yu
In this paper, we present and illustrate some new tools for rigorously analyzing training data selection methods. These tools focus on the information theoretic losses that occur w…
cs.LG2019★ 8 cited
Information Losses in Neural Classifiers from Sampling
Brandon Foggo, Nanpeng Yu, Jie Shi +1
This paper considers the subject of information losses arising from the finite datasets used in the training of neural classifiers. It proves a relationship between such losses as…