Towards a mathematical understanding of learning from few examples with
nonlinear feature maps

Towards a mathematical understanding of learning from few examples with nonlinear feature maps

7 November 2022

Oliver J. Sutton

Alexander N. Gorban

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Papers citing "Towards a mathematical understanding of learning from few examples with nonlinear feature maps"

8 / 8 papers shown

Title
High-dimensional separability for one- and few-shot learning Alexander N. Gorban Bogdan Grechuk Evgeny M. Mirkes Sergey V. Stasenko I. Tyukin DRL 69 23 0 28 Jun 2021
EfficientNetV2: Smaller Models and Faster Training Mingxing Tan Quoc V. Le EgoV 125 2,720 0 01 Apr 2021
Generalizing from a Few Examples: A Survey on Few-Shot Learning Yaqing Wang Quanming Yao James T. Kwok L. Ni 90 1,816 0 10 Apr 2019
MobileNetV2: Inverted Residuals and Linear Bottlenecks Mark Sandler Andrew G. Howard Menglong Zhu A. Zhmoginov Liang-Chieh Chen 204 19,333 0 13 Jan 2018
Blessing of dimensionality: mathematical foundations of the statistical physics of data A. N. Gorban I. Tyukin 64 150 0 10 Jan 2018
Stochastic Separation Theorems A. N. Gorban I. Tyukin 53 50 0 03 Mar 2017
Understanding deep learning requires rethinking generalization Chiyuan Zhang Samy Bengio Moritz Hardt Benjamin Recht Oriol Vinyals HAI 348 4,635 0 10 Nov 2016
Matching Networks for One Shot Learning Oriol Vinyals Charles Blundell Timothy Lillicrap Koray Kavukcuoglu Daan Wierstra VLM 375 7,333 0 13 Jun 2016

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