Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced

4 June 2018

Papers citing "Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced"

50 / 73 papers shown

Title
The late-stage training dynamics of (stochastic) subgradient descent on homogeneous neural networks Sholom Schechtman Nicolas Schreuder 203 0 0 08 Feb 2025
An Invitation to Neuroalgebraic Geometry Giovanni Luca Marchetti Vahid Shahverdi Stefano Mereta Matthew Trager Kathlén Kohn 119 2 0 31 Jan 2025
Geometry and Optimization of Shallow Polynomial Networks Yossi Arjevani Joan Bruna Joe Kileel Elzbieta Polak Matthew Trager 36 1 0 10 Jan 2025
How Feature Learning Can Improve Neural Scaling Laws Blake Bordelon Alexander B. Atanasov Cengiz Pehlevan 57 12 0 26 Sep 2024
Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks Hristo Papazov Scott Pesme Nicolas Flammarion 38 5 0 08 Mar 2024
Provably Accelerating Ill-Conditioned Low-rank Estimation via Scaled Gradient Descent, Even with Overparameterization Cong Ma Xingyu Xu Tian Tong Yuejie Chi 18 9 0 09 Oct 2023
How Over-Parameterization Slows Down Gradient Descent in Matrix Sensing: The Curses of Symmetry and Initialization Nuoya Xiong Lijun Ding Simon S. Du 48 11 0 03 Oct 2023
Early Neuron Alignment in Two-layer ReLU Networks with Small Initialization Hancheng Min Enrique Mallada René Vidal MLT 36 19 0 24 Jul 2023
Addressing caveats of neural persistence with deep graph persistence Leander Girrbach Anders Christensen Ole Winther Zeynep Akata A. Sophia Koepke GNN 28 1 0 20 Jul 2023
FedBug: A Bottom-Up Gradual Unfreezing Framework for Federated Learning Chia-Hsiang Kao Yu-Chiang Frank Wang FedML 26 1 0 19 Jul 2023
Improving Convergence and Generalization Using Parameter Symmetries Bo Zhao Robert Mansel Gower Robin Walters Rose Yu MoMe 33 13 0 22 May 2023
Gradient Descent Monotonically Decreases the Sharpness of Gradient Flow Solutions in Scalar Networks and Beyond Itai Kreisler Mor Shpigel Nacson Daniel Soudry Y. Carmon 33 13 0 22 May 2023
SFP: Spurious Feature-targeted Pruning for Out-of-Distribution Generalization Yingchun Wang Jingcai Guo Yi Liu Song Guo Weizhan Zhang Xiangyong Cao Qinghua Zheng AAML OODD 33 11 0 19 May 2023
Convergence of Alternating Gradient Descent for Matrix Factorization R. Ward T. Kolda 22 6 0 11 May 2023
On the Stepwise Nature of Self-Supervised Learning James B. Simon Maksis Knutins Liu Ziyin Daniel Geisz Abraham J. Fetterman Joshua Albrecht SSL 37 30 0 27 Mar 2023
Active Self-Supervised Learning: A Few Low-Cost Relationships Are All You Need Vivien A. Cabannes Léon Bottou Yann LeCun Randall Balestriero 48 13 0 27 Mar 2023
Critical Points and Convergence Analysis of Generative Deep Linear Networks Trained with Bures-Wasserstein Loss Pierre Bréchet Katerina Papagiannouli Jing An Guido Montúfar 33 3 0 06 Mar 2023
Over-Parameterization Exponentially Slows Down Gradient Descent for Learning a Single Neuron Weihang Xu S. Du 37 16 0 20 Feb 2023
The Geometry of Neural Nets' Parameter Spaces Under Reparametrization Agustinus Kristiadi Felix Dangel Philipp Hennig 32 11 0 14 Feb 2023
How to prepare your task head for finetuning Yi Ren Shangmin Guo Wonho Bae Danica J. Sutherland 24 14 0 11 Feb 2023
On a continuous time model of gradient descent dynamics and instability in deep learning Mihaela Rosca Yan Wu Chongli Qin Benoit Dherin 20 7 0 03 Feb 2023
Effects of Data Geometry in Early Deep Learning Saket Tiwari George Konidaris 79 7 0 29 Dec 2022
Infinite-width limit of deep linear neural networks Lénaïc Chizat Maria Colombo Xavier Fernández-Real Alessio Figalli 31 14 0 29 Nov 2022
Mechanistic Mode Connectivity Ekdeep Singh Lubana Eric J. Bigelow Robert P. Dick David M. Krueger Hidenori Tanaka 32 45 0 15 Nov 2022
Symmetries, flat minima, and the conserved quantities of gradient flow Bo Zhao I. Ganev Robin Walters Rose Yu Nima Dehmamy 47 16 0 31 Oct 2022
Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models Hong Liu Sang Michael Xie Zhiyuan Li Tengyu Ma AI4CE 40 49 0 25 Oct 2022
Surgical Fine-Tuning Improves Adaptation to Distribution Shifts Yoonho Lee Annie S. Chen Fahim Tajwar Ananya Kumar Huaxiu Yao Percy Liang Chelsea Finn OOD 61 198 0 20 Oct 2022
Wasserstein Barycenter-based Model Fusion and Linear Mode Connectivity of Neural Networks A. K. Akash Sixu Li Nicolas García Trillos 34 12 0 13 Oct 2022
Boosting Adversarial Robustness From The Perspective of Effective Margin Regularization Ziquan Liu Antoni B. Chan AAML 33 5 0 11 Oct 2022
Implicit Bias of Large Depth Networks: a Notion of Rank for Nonlinear Functions Arthur Jacot 36 25 0 29 Sep 2022
Magnitude and Angle Dynamics in Training Single ReLU Neurons Sangmin Lee Byeongsu Sim Jong Chul Ye MLT 96 6 0 27 Sep 2022
A Validation Approach to Over-parameterized Matrix and Image Recovery Lijun Ding Zhen Qin Liwei Jiang Jinxin Zhou Zhihui Zhu 48 13 0 21 Sep 2022
Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization) Zhenyu Zhu Fanghui Liu Grigorios G. Chrysos V. Cevher 39 19 0 15 Sep 2022
On the Implicit Bias in Deep-Learning Algorithms Gal Vardi FedML AI4CE 34 72 0 26 Aug 2022
Implicit Regularization with Polynomial Growth in Deep Tensor Factorization Kais Hariz Hachem Kadri Stéphane Ayache Maher Moakher Thierry Artières 26 2 0 18 Jul 2022
Blessing of Nonconvexity in Deep Linear Models: Depth Flattens the Optimization Landscape Around the True Solution Jianhao Ma S. Fattahi 44 5 0 15 Jul 2022
Symmetry Teleportation for Accelerated Optimization B. Zhao Nima Dehmamy Robin Walters Rose Yu ODL 23 20 0 21 May 2022
Algorithmic Regularization in Model-free Overparametrized Asymmetric Matrix Factorization Liwei Jiang Yudong Chen Lijun Ding 43 26 0 06 Mar 2022
Understanding Deep Contrastive Learning via Coordinate-wise Optimization Yuandong Tian 52 34 0 29 Jan 2022
Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks Noam Razin Asaf Maman Nadav Cohen 46 29 0 27 Jan 2022
Regularization by Misclassification in ReLU Neural Networks Elisabetta Cornacchia Jan Hązła Ido Nachum Amir Yehudayoff NoLa 25 2 0 03 Nov 2021
Neural Networks as Kernel Learners: The Silent Alignment Effect Alexander B. Atanasov Blake Bordelon Cengiz Pehlevan MLT 26 75 0 29 Oct 2021
On the Regularization of Autoencoders Harald Steck Dario Garcia-Garcia SSL AI4CE 30 4 0 21 Oct 2021
Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect Yuqing Wang Minshuo Chen T. Zhao Molei Tao AI4CE 57 40 0 07 Oct 2021
On Margin Maximization in Linear and ReLU Networks Gal Vardi Ohad Shamir Nathan Srebro 52 28 0 06 Oct 2021
Nonconvex Factorization and Manifold Formulations are Almost Equivalent in Low-rank Matrix Optimization Yuetian Luo Xudong Li Anru R. Zhang 33 9 0 03 Aug 2021
Convergence analysis for gradient flows in the training of artificial neural networks with ReLU activation Arnulf Jentzen Adrian Riekert 27 23 0 09 Jul 2021
A Mechanism for Producing Aligned Latent Spaces with Autoencoders Saachi Jain Adityanarayanan Radhakrishnan Caroline Uhler 24 9 0 29 Jun 2021
Global Convergence of Gradient Descent for Asymmetric Low-Rank Matrix Factorization Tian-Chun Ye S. Du 21 46 0 27 Jun 2021
Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization Tianyi Liu Yan Li S. Wei Enlu Zhou T. Zhao 21 13 0 24 Feb 2021