38 citations · 57 across the 7 of their papers we have counts for
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stat.ML2019★ 1 cited
Degrees of freedom for off-the-grid sparse estimation
Clarice Poon, Gabriel Peyré
A central question in modern machine learning and imaging sciences is to quantify the number of effective parameters of vastly over-parameterized models. The degrees of freedom is…
stat.ML2019
Ground Metric Learning on Graphs
Matthieu Heitz, Nicolas Bonneel, David Coeurjolly +2
Optimal transport (OT) distances between probability distributions are parameterized by the ground metric they use between observations. Their relevance for real-life applications…