PERP: Rethinking the Prune-Retrain Paradigm in the Era of LLMs

23 December 2023

Papers citing "PERP: Rethinking the Prune-Retrain Paradigm in the Era of LLMs"

6 / 6 papers shown

Title
On the Byzantine-Resilience of Distillation-Based Federated Learning Christophe Roux Max Zimmer S. Pokutta AAML 28 1 0 19 Feb 2024
Instant Soup: Cheap Pruning Ensembles in A Single Pass Can Draw Lottery Tickets from Large Models A. Jaiswal Shiwei Liu Tianlong Chen Ying Ding Zhangyang Wang VLM 16 18 0 18 Jun 2023
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks Torsten Hoefler Dan Alistarh Tal Ben-Nun Nikoli Dryden Alexandra Peste MQ 125 526 0 31 Jan 2021
Comparing Rewinding and Fine-tuning in Neural Network Pruning Alex Renda Jonathan Frankle Michael Carbin 214 354 0 05 Mar 2020
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Jinpeng Wang Amanpreet Singh Julian Michael Felix Hill Omer Levy Samuel R. Bowman ELM 292 6,003 0 20 Apr 2018
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 279 9,997 0 01 Sep 2014