On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization

19 February 2018

Papers citing "On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization"

50 / 119 papers shown

Title
Make Haste Slowly: A Theory of Emergent Structured Mixed Selectivity in Feature Learning ReLU Networks Devon Jarvis Richard Klein Benjamin Rosman Andrew M. Saxe MLT 66 1 0 08 Mar 2025
An Invitation to Neuroalgebraic Geometry G. Marchetti V. Shahverdi Stefano Mereta Matthew Trager Kathlén Kohn 119 2 0 31 Jan 2025
Optimization Insights into Deep Diagonal Linear Networks Hippolyte Labarrière C. Molinari Lorenzo Rosasco S. Villa Cristian Vega 76 0 0 21 Dec 2024
From Lazy to Rich: Exact Learning Dynamics in Deep Linear Networks Clémentine Dominé Nicolas Anguita A. Proca Lukas Braun D. Kunin P. Mediano Andrew M. Saxe 38 3 0 22 Sep 2024
Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation Can Yaras Peng Wang Laura Balzano Qing Qu AI4CE 37 13 0 06 Jun 2024
Structure-Preserving Network Compression Via Low-Rank Induced Training Through Linear Layers Composition Xitong Zhang Ismail R. Alkhouri Rongrong Wang 41 0 0 06 May 2024
Faster Convergence of Stochastic Accelerated Gradient Descent under Interpolation Aaron Mishkin Mert Pilanci Mark Schmidt 64 1 0 03 Apr 2024
Understanding the Double Descent Phenomenon in Deep Learning Marc Lafon Alexandre Thomas 25 2 0 15 Mar 2024
RepQ: Generalizing Quantization-Aware Training for Re-Parametrized Architectures Anastasiia Prutianova Alexey Zaytsev Chung-Kuei Lee Fengyu Sun Ivan Koryakovskiy MQ 13 0 0 09 Nov 2023
Enhancing Cross-Category Learning in Recommendation Systems with Multi-Layer Embedding Training Selim F. Yilmaz Benjamin Ghaemmaghami A. Singh Benjamin Cho Leo Orshansky Lei Deng Michael Orshansky AI4TS 28 0 0 27 Sep 2023
Critical Learning Periods Emerge Even in Deep Linear Networks Michael Kleinman Alessandro Achille Stefano Soatto 44 3 0 23 Aug 2023
Early Neuron Alignment in Two-layer ReLU Networks with Small Initialization Hancheng Min Enrique Mallada René Vidal MLT 34 19 0 24 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
Multiplicative update rules for accelerating deep learning training and increasing robustness Manos Kirtas Nikolaos Passalis Anastasios Tefas AAML OOD 36 2 0 14 Jul 2023
No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models Jean Kaddour Oscar Key Piotr Nawrot Pasquale Minervini Matt J. Kusner 22 41 0 12 Jul 2023
ReLU Neural Networks with Linear Layers are Biased Towards Single- and Multi-Index Models Suzanna Parkinson Greg Ongie Rebecca Willett 65 6 0 24 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
Robust Implicit Regularization via Weight Normalization H. Chou Holger Rauhut Rachel A. Ward 33 7 0 09 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
Sparse-IFT: Sparse Iso-FLOP Transformations for Maximizing Training Efficiency Vithursan Thangarasa Shreyas Saxena Abhay Gupta Sean Lie 31 3 0 21 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
Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities Aleksandr Beznosikov Martin Takáč Alexander Gasnikov 29 10 0 15 Feb 2023
On a continuous time model of gradient descent dynamics and instability in deep learning Mihaela Rosca Yan Wu Chongli Qin Benoit Dherin 18 7 0 03 Feb 2023
A Survey on Efficient Training of Transformers Bohan Zhuang Jing Liu Zizheng Pan Haoyu He Yuetian Weng Chunhua Shen 31 47 0 02 Feb 2023
On the Lipschitz Constant of Deep Networks and Double Descent Matteo Gamba Hossein Azizpour Marten Bjorkman 31 7 0 28 Jan 2023
PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization Sanae Lotfi Marc Finzi Sanyam Kapoor Andres Potapczynski Micah Goldblum A. Wilson BDL MLT AI4CE 29 51 0 24 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
Adaptive Compression for Communication-Efficient Distributed Training Maksim Makarenko Elnur Gasanov Rustem Islamov Abdurakhmon Sadiev Peter Richtárik 39 13 0 31 Oct 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
On the optimization and generalization of overparameterized implicit neural networks Tianxiang Gao Hongyang Gao MLT AI4CE 19 3 0 30 Sep 2022
Interneurons accelerate learning dynamics in recurrent neural networks for statistical adaptation David Lipshutz Cengiz Pehlevan D. Chklovskii 25 11 0 21 Sep 2022
Deep Linear Networks can Benignly Overfit when Shallow Ones Do Niladri S. Chatterji Philip M. Long 23 8 0 19 Sep 2022
On the Implicit Bias in Deep-Learning Algorithms Gal Vardi FedML AI4CE 34 72 0 26 Aug 2022
KL-divergence Based Deep Learning for Discrete Time Model Li Liu Xiangeng Fang Di Wang Weijing Tang Kevin He 18 1 0 10 Aug 2022
The Neural Race Reduction: Dynamics of Abstraction in Gated Networks Andrew M. Saxe Shagun Sodhani Sam Lewallen AI4CE 30 34 0 21 Jul 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
Utilizing Excess Resources in Training Neural Networks Amit Henig Raja Giryes 53 0 0 12 Jul 2022
q-Learning in Continuous Time Yanwei Jia X. Zhou OffRL 51 68 0 02 Jul 2022
Non-convex online learning via algorithmic equivalence Udaya Ghai Zhou Lu Elad Hazan 14 8 0 30 May 2022
Do Residual Neural Networks discretize Neural Ordinary Differential Equations? Michael E. Sander Pierre Ablin Gabriel Peyré 35 25 0 29 May 2022
Embedding Principle in Depth for the Loss Landscape Analysis of Deep Neural Networks Zhiwei Bai Tao Luo Z. Xu Yaoyu Zhang 31 4 0 26 May 2022
Empirical Phase Diagram for Three-layer Neural Networks with Infinite Width Hanxu Zhou Qixuan Zhou Zhenyuan Jin Tao Luo Yaoyu Zhang Zhi-Qin John Xu 25 20 0 24 May 2022
Symmetry Teleportation for Accelerated Optimization B. Zhao Nima Dehmamy Robin Walters Rose Yu ODL 23 20 0 21 May 2022
RepSR: Training Efficient VGG-style Super-Resolution Networks with Structural Re-Parameterization and Batch Normalization Xintao Wang Chao Dong Ying Shan 24 48 0 11 May 2022
Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGs Aaron Courville Wei Liu Kewei Tu 21 8 0 01 May 2022
Online Convolutional Re-parameterization Mu Hu Junyi Feng Jiashen Hua Baisheng Lai Jianqiang Huang Xiaojin Gong Xiansheng Hua 24 26 0 02 Apr 2022
ELLE: Efficient Lifelong Pre-training for Emerging Data Yujia Qin Jiajie Zhang Yankai Lin Zhiyuan Liu Peng Li Maosong Sun Jie Zhou 30 67 0 12 Mar 2022
Side Effects of Learning from Low-dimensional Data Embedded in a Euclidean Space Juncai He R. Tsai Rachel A. Ward 36 8 0 01 Mar 2022
Benefit of Interpolation in Nearest Neighbor Algorithms Yue Xing Qifan Song Guang Cheng 14 28 0 23 Feb 2022
Understanding Deep Contrastive Learning via Coordinate-wise Optimization Yuandong Tian 52 34 0 29 Jan 2022