Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting
Model Hubs

Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs

20 October 2021

Yong Liu

Michael I. Jordan

Papers citing "Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs"

7 / 7 papers shown

Title
Capability Instruction Tuning: A New Paradigm for Dynamic LLM Routing Yi-Kai Zhang De-Chuan Zhan Han-Jia Ye ALM ELM LRM 29 1 0 24 Feb 2025
Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How Sebastian Pineda Arango Fabio Ferreira Arlind Kadra Frank Hutter Frank Hutter Josif Grabocka 11 15 0 06 Jun 2023
MLP-Mixer: An all-MLP Architecture for Vision Ilya O. Tolstikhin N. Houlsby Alexander Kolesnikov Lucas Beyer Xiaohua Zhai ... Andreas Steiner Daniel Keysers Jakob Uszkoreit Mario Lucic Alexey Dosovitskiy 239 2,554 0 04 May 2021
Pre-trained Models for Natural Language Processing: A Survey Xipeng Qiu Tianxiang Sun Yige Xu Yunfan Shao Ning Dai Xuanjing Huang LM&MA VLM 224 1,281 0 18 Mar 2020
Improved Baselines with Momentum Contrastive Learning Xinlei Chen Haoqi Fan Ross B. Girshick Kaiming He SSL 229 3,029 0 09 Mar 2020
Transferability and Hardness of Supervised Classification Tasks Anh Tran Cuong V Nguyen Tal Hassner 134 163 0 21 Aug 2019
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 294 6,003 0 20 Apr 2018