Why Random Pruning Is All We Need to Start Sparse

5 October 2022

Papers citing "Why Random Pruning Is All We Need to Start Sparse"

13 / 13 papers shown

Title
Sign-In to the Lottery: Reparameterizing Sparse Training From Scratch Advait Gadhikar Tom Jacobs Chao Zhou R. Burkholz 27 0 0 17 Apr 2025
Trading-off Accuracy and Communication Cost in Federated Learning Mattia Jacopo Villani Emanuele Natale Frederik Mallmann-Trenn FedML 68 0 0 18 Mar 2025
Mask in the Mirror: Implicit Sparsification Tom Jacobs R. Burkholz 40 3 0 19 Aug 2024
Sparsifying dimensionality reduction of PDE solution data with Bregman learning T. J. Heeringa Christoph Brune Mengwu Guo 24 0 0 18 Jun 2024
Cyclic Sparse Training: Is it Enough? Advait Gadhikar Sree Harsha Nelaturu R. Burkholz CLL 33 0 0 04 Jun 2024
Spectral Graph Pruning Against Over-Squashing and Over-Smoothing Adarsh Jamadandi Celia Rubio-Madrigal R. Burkholz 30 1 0 06 Apr 2024
FOCIL: Finetune-and-Freeze for Online Class Incremental Learning by Training Randomly Pruned Sparse Experts Murat Onur Yildirim Elif Ceren Gok Yildirim D. Mocanu Joaquin Vanschoren CLL 29 0 0 13 Mar 2024
Masks, Signs, And Learning Rate Rewinding Advait Gadhikar R. Burkholz 50 8 0 29 Feb 2024
A Simple and Effective Pruning Approach for Large Language Models Mingjie Sun Zhuang Liu Anna Bair J. Zico Kolter 50 353 0 20 Jun 2023
Understanding MLP-Mixer as a Wide and Sparse MLP Tomohiro Hayase Ryo Karakida MoE 24 4 0 02 Jun 2023
Understanding Sparse Neural Networks from their Topology via Multipartite Graph Representations Elia Cunegatti Matteo Farina Doina Bucur Giovanni Iacca 16 1 0 26 May 2023
Comparing Rewinding and Fine-tuning in Neural Network Pruning Alex Renda Jonathan Frankle Michael Carbin 222 382 0 05 Mar 2020
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 282 39,170 0 01 Sep 2014