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Topological Data Analysis of Decision Boundaries with Application to Model Selection
Karthikeyan Ramamurthy · Kush Varshney · Krishnan Mody
We propose the labeled Cech complex, the plain labeled Vietoris-Rips complex, and the locally scaled labeled Vietoris-Rips complex to perform persistent homology inference of decision boundaries in classification tasks. We provide theoretical conditions and analysis for recovering the homology of a decision boundary from samples. Our main objective is quantification of deep neural network complexity to enable matching of datasets to pre-trained models to facilitate the functioning of AI marketplaces; we report results for experiments using MNIST, FashionMNIST, and CIFAR10.
Author Information
Karthikeyan Ramamurthy (IBM Research)
Kush Varshney (IBM Research AI)
Krishnan Mody (New York University)
Related Events (a corresponding poster, oral, or spotlight)
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2019 Poster: Topological Data Analysis of Decision Boundaries with Application to Model Selection »
Wed. Jun 12th 01:30 -- 04:00 AM Room Pacific Ballroom #124
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