Gradient Flow in Sparse Neural Networks and How Lottery Tickets Win

Gradient Flow in Sparse Neural Networks and How Lottery Tickets Win

7 October 2020

Yani Andrew Ioannou

Yann N. Dauphin

Papers citing "Gradient Flow in Sparse Neural Networks and How Lottery Tickets Win"

17 / 17 papers shown

Title
Sparse Training from Random Initialization: Aligning Lottery Ticket Masks using Weight Symmetry Mohammed Adnan Rohan Jain Ekansh Sharma Rahul Krishnan Yani Andrew Ioannou 56 0 0 08 May 2025
Information Consistent Pruning: How to Efficiently Search for Sparse Networks? Soheil Gharatappeh S. Y. Sekeh 54 0 0 28 Jan 2025
Pushing the Limits of Sparsity: A Bag of Tricks for Extreme Pruning Andy Li A. Durrant Milan Markovic Lu Yin Georgios Leontidis Tianlong Chen Lu Yin Georgios Leontidis 75 0 0 20 Nov 2024
Navigating Extremes: Dynamic Sparsity in Large Output Spaces Nasib Ullah Erik Schultheis Mike Lasby Yani Andrew Ioannou Rohit Babbar 33 0 0 05 Nov 2024
Dynamic Sparse Training versus Dense Training: The Unexpected Winner in Image Corruption Robustness Boqian Wu Q. Xiao Shunxin Wang N. Strisciuglio Mykola Pechenizkiy M. V. Keulen D. Mocanu Elena Mocanu OOD 3DH 52 0 0 03 Oct 2024
NTK-SAP: Improving neural network pruning by aligning training dynamics Yite Wang Dawei Li Ruoyu Sun 31 19 0 06 Apr 2023
Considering Layerwise Importance in the Lottery Ticket Hypothesis Benjamin Vandersmissen José Oramas 15 1 0 22 Feb 2023
Balance is Essence: Accelerating Sparse Training via Adaptive Gradient Correction Bowen Lei Dongkuan Xu Ruqi Zhang Shuren He Bani Mallick 27 6 0 09 Jan 2023
Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask? Mansheej Paul F. Chen Brett W. Larsen Jonathan Frankle Surya Ganguli Gintare Karolina Dziugaite UQCV 25 38 0 06 Oct 2022
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity Shiwei Liu Tianlong Chen Zahra Atashgahi Xiaohan Chen Ghada Sokar Elena Mocanu Mykola Pechenizkiy Zhangyang Wang D. Mocanu OOD 28 49 0 28 Jun 2021
Recent Advances on Neural Network Pruning at Initialization Huan Wang Can Qin Yue Bai Yulun Zhang Yun Fu CVBM 31 64 0 11 Mar 2021
Sparse Training Theory for Scalable and Efficient Agents D. Mocanu Elena Mocanu T. Pinto Selima Curci Phuong H. Nguyen M. Gibescu D. Ernst Z. Vale 45 17 0 02 Mar 2021
Truly Sparse Neural Networks at Scale Selima Curci D. Mocanu Mykola Pechenizkiy 25 19 0 02 Feb 2021
Efficient Estimation of Influence of a Training Instance Sosuke Kobayashi Sho Yokoi Jun Suzuki Kentaro Inui TDI 27 15 0 08 Dec 2020
Comparing Rewinding and Fine-tuning in Neural Network Pruning Alex Renda Jonathan Frankle Michael Carbin 224 382 0 05 Mar 2020
The large learning rate phase of deep learning: the catapult mechanism Aitor Lewkowycz Yasaman Bahri Ethan Dyer Jascha Narain Sohl-Dickstein Guy Gur-Ari ODL 159 234 0 04 Mar 2020
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks Lechao Xiao Yasaman Bahri Jascha Narain Sohl-Dickstein S. Schoenholz Jeffrey Pennington 220 348 0 14 Jun 2018