Geometry of Linear Convolutional Networks

3 August 2021

Matthew Trager

Papers citing "Geometry of Linear Convolutional Networks"

24 / 24 papers shown

Title
Understanding Mode Connectivity via Parameter Space Symmetry B. Zhao Nima Dehmamy Robin Walters Rose Yu 211 7 0 29 May 2025
Geometry of Linear Neural Networks: Equivariance and Invariance under Permutation Groups Kathlén Kohn Anna-Laura Sattelberger Vahid Shahverdi 56 3 0 24 Sep 2023
Optimization Theory for ReLU Neural Networks Trained with Normalization Layers Yonatan Dukler Quanquan Gu Guido Montúfar 56 30 0 11 Jun 2020
Wasserstein Distance to Independence Models Turku Ozlum cCelik Asgar Jamneshan Guido Montúfar Bernd Sturmfels Lorenzo Venturello 21 26 0 15 Mar 2020
Learning deep linear neural networks: Riemannian gradient flows and convergence to global minimizers B. Bah Holger Rauhut Ulrich Terstiege Michael Westdickenberg MLT 32 63 0 12 Oct 2019
Pure and Spurious Critical Points: a Geometric Study of Linear Networks Matthew Trager Kathlén Kohn Joan Bruna 49 28 0 03 Oct 2019
Optimal Transport to a Variety Turku Ozlum Celik Asgar Jamneshan Guido Montúfar Bernd Sturmfels Lorenzo Venturello OT 37 11 0 25 Sep 2019
Gradient Dynamics of Shallow Univariate ReLU Networks Francis Williams Matthew Trager Claudio Silva Daniele Panozzo Denis Zorin Joan Bruna 53 80 0 18 Jun 2019
Depth creates no more spurious local minima Li Zhang 37 19 0 28 Jan 2019
A Convergence Theory for Deep Learning via Over-Parameterization Zeyuan Allen-Zhu Yuanzhi Li Zhao Song AI4CE ODL 242 1,462 0 09 Nov 2018
The loss surface of deep linear networks viewed through the algebraic geometry lens D. Mehta Tianran Chen Tingting Tang J. Hauenstein ODL 48 32 0 17 Oct 2018
A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks Sanjeev Arora Nadav Cohen Noah Golowich Wei Hu 110 290 0 04 Oct 2018
Gradient Descent Provably Optimizes Over-parameterized Neural Networks S. Du Xiyu Zhai Barnabás Póczós Aarti Singh MLT ODL 214 1,272 0 04 Oct 2018
Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview Yuejie Chi Yue M. Lu Yuxin Chen 58 425 0 25 Sep 2018
Implicit Bias of Gradient Descent on Linear Convolutional Networks Suriya Gunasekar Jason D. Lee Daniel Soudry Nathan Srebro MDE 124 411 0 01 Jun 2018
On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization Sanjeev Arora Nadav Cohen Elad Hazan 99 484 0 19 Feb 2018
Deep linear neural networks with arbitrary loss: All local minima are global T. Laurent J. V. Brecht ODL 55 136 0 05 Dec 2017
The Implicit Bias of Gradient Descent on Separable Data Daniel Soudry Elad Hoffer Mor Shpigel Nacson Suriya Gunasekar Nathan Srebro 153 916 0 27 Oct 2017
Maximum likelihood estimation of the Latent Class Model through model boundary decomposition E. Allman H. Cervantes R. Evans Serkan Hocsten Kaie Kubjas Daniela Lemke J. Rhodes Piotr Zwiernik 33 15 0 04 Oct 2017
Mixtures and products in two graphical models A. Seigal Guido Montúfar TPM 29 17 0 15 Sep 2017
Depth Creates No Bad Local Minima Haihao Lu Kenji Kawaguchi ODL FAtt 71 121 0 27 Feb 2017
Identity Matters in Deep Learning Moritz Hardt Tengyu Ma OOD 81 398 0 14 Nov 2016
Deep Learning without Poor Local Minima Kenji Kawaguchi ODL 219 923 0 23 May 2016
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks Andrew M. Saxe James L. McClelland Surya Ganguli ODL 165 1,844 0 20 Dec 2013