MO3.R2.4

Rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descent

Michael Kohler, Technical University of Darmstadt, Germany; Adam Krzyzak, Concordia University, Canada; Benjamin Walter, Technical University of Darmstadt, Germany
Session:
Classification and Regression
Track:
8: Machine Learning
Location:
Ypsilon I-II-III
Presentation Time:
Mon, 8 Jul, 15:35 - 15:55
Session Chair:
Adam Krzyzak, Concordia University
Presentation
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Discussion
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Session MO3.R2
MO3.R2.1: Effect of Weight Quantization on Learning Models by Typical Case Analysis
Shuhei Kashiwamura, The University of Tokyo, Japan; Ayaka Sakata, The Institute of Statistical Mathematics, Japan; Masaaki Imaizumi, The University of Tokyo, Japan
MO3.R2.2: Sharp information-theoretic thresholds for shuffled linear regression
Leon Lufkin, Yihong Wu, Yale University, United States; Jiaming Xu, Duke University, United States
MO3.R2.3: Data-Driven Estimation of the False Positive Rate of the Bayes Binary Classifier via Soft Labels
Minoh Jeong, Martina Cardone, University of Minnesota, United States; Alex Dytso, Qualcomm Flarion Technology, Inc., United States
MO3.R2.4: Rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descent
Michael Kohler, Technical University of Darmstadt, Germany; Adam Krzyzak, Concordia University, Canada; Benjamin Walter, Technical University of Darmstadt, Germany
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