Embedding principle of homogeneous neural network for classification problem

18 May 2025

Papers citing "Embedding principle of homogeneous neural network for classification problem"

11 / 11 papers shown

Title
Embedding Principle of Loss Landscape of Deep Neural Networks Yaoyu Zhang Zhongwang Zhang Yaoyu Zhang Z. Xu 29 36 0 30 May 2021
Directional convergence and alignment in deep learning Ziwei Ji Matus Telgarsky 41 167 0 11 Jun 2020
Conservative set valued fields, automatic differentiation, stochastic gradient method and deep learning Jérôme Bolte Edouard Pauwels 32 128 0 23 Sep 2019
Gradient Descent Maximizes the Margin of Homogeneous Neural Networks Kaifeng Lyu Jian Li 68 332 0 13 Jun 2019
Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models Mor Shpigel Nacson Suriya Gunasekar Jason D. Lee Nathan Srebro Daniel Soudry 45 92 0 17 May 2019
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel Colin Wei Jason D. Lee Qiang Liu Tengyu Ma 131 245 0 12 Oct 2018
Gradient descent aligns the layers of deep linear networks Ziwei Ji Matus Telgarsky 105 250 0 04 Oct 2018
Stochastic subgradient method converges on tame functions Damek Davis Dmitriy Drusvyatskiy Sham Kakade Jason D. Lee 34 251 0 20 Apr 2018
Convergence of Gradient Descent on Separable Data Mor Shpigel Nacson Jason D. Lee Suriya Gunasekar Pedro H. P. Savarese Nathan Srebro Daniel Soudry 60 167 0 05 Mar 2018
Characterizing Implicit Bias in Terms of Optimization Geometry Suriya Gunasekar Jason D. Lee Daniel Soudry Nathan Srebro AI4CE 62 404 0 22 Feb 2018
The Implicit Bias of Gradient Descent on Separable Data Daniel Soudry Elad Hoffer Mor Shpigel Nacson Suriya Gunasekar Nathan Srebro 74 908 0 27 Oct 2017