Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients

16 February 2022

Papers citing "Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients"

8 / 8 papers shown

Title
Rapid Deployment of DNNs for Edge Computing via Structured Pruning at Initialization Bailey J. Eccles Leon Wong Blesson Varghese 33 2 0 22 Apr 2024
Magnitude Attention-based Dynamic Pruning Jihye Back Namhyuk Ahn Jang-Hyun Kim 25 2 0 08 Jun 2023
NTK-SAP: Improving neural network pruning by aligning training dynamics Yite Wang Dawei Li Ruoyu Sun 28 19 0 06 Apr 2023
Balanced Training for Sparse GANs Yite Wang Jing Wu N. Hovakimyan Ruoyu Sun 32 9 0 28 Feb 2023
FedCliP: Federated Learning with Client Pruning Beibei Li Zerui Shao Ao Liu Peiran Wang FedML 37 1 0 17 Jan 2023
Rare Gems: Finding Lottery Tickets at Initialization Kartik K. Sreenivasan Jy-yong Sohn Liu Yang Matthew Grinde Alliot Nagle Hongyi Wang Eric P. Xing Kangwook Lee Dimitris Papailiopoulos 16 42 0 24 Feb 2022
Comparing Rewinding and Fine-tuning in Neural Network Pruning Alex Renda Jonathan Frankle Michael Carbin 222 382 0 05 Mar 2020
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 314 11,681 0 09 Mar 2017