ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2105.02221
  4. Cited By
How Fine-Tuning Allows for Effective Meta-Learning

How Fine-Tuning Allows for Effective Meta-Learning

5 May 2021
Kurtland Chua
Qi Lei
Jason D. Lee
ArXivPDFHTML

Papers citing "How Fine-Tuning Allows for Effective Meta-Learning"

14 / 14 papers shown
Title
GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds
GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds
Aoran Chen
Yang Feng
31
0
0
05 May 2025
Multi-Task Dynamic Pricing in Credit Market with Contextual Information
Multi-Task Dynamic Pricing in Credit Market with Contextual Information
Adel Javanmard
Jingwei Ji
Renyuan Xu
39
1
0
18 Oct 2024
Rethinking Meta-Learning from a Learning Lens
Rethinking Meta-Learning from a Learning Lens
Wenwen Qiang
Jingyao Wang
Chuxiong Sun
Hui Xiong
Jiangmeng Li
48
1
0
13 Sep 2024
Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis
Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis
Yufan Li
Subhabrata Sen
Ben Adlam
MLT
51
1
0
18 Apr 2024
Initializing Services in Interactive ML Systems for Diverse Users
Initializing Services in Interactive ML Systems for Diverse Users
Avinandan Bose
Mihaela Curmei
Daniel L. Jiang
Jamie Morgenstern
Sarah Dean
Lillian J. Ratliff
Maryam Fazel
21
5
0
19 Dec 2023
Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained
  Models
Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models
Guillermo Ortiz-Jiménez
Alessandro Favero
P. Frossard
MoMe
51
110
0
22 May 2023
Multi-Task Imitation Learning for Linear Dynamical Systems
Multi-Task Imitation Learning for Linear Dynamical Systems
Thomas T. Zhang
Katie Kang
Bruce D. Lee
Claire Tomlin
Sergey Levine
Stephen Tu
Nikolai Matni
35
23
0
01 Dec 2022
On the Impossible Safety of Large AI Models
On the Impossible Safety of Large AI Models
El-Mahdi El-Mhamdi
Sadegh Farhadkhani
R. Guerraoui
Nirupam Gupta
L. Hoang
Rafael Pinot
Sébastien Rouault
John Stephan
30
31
0
30 Sep 2022
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning
Liam Collins
Hamed Hassani
Aryan Mokhtari
Sanjay Shakkottai
FedML
32
75
0
27 May 2022
Provable Lifelong Learning of Representations
Provable Lifelong Learning of Representations
Xinyuan Cao
Weiyang Liu
Santosh Vempala
CLL
21
13
0
27 Oct 2021
A Theoretical Analysis of Fine-tuning with Linear Teachers
A Theoretical Analysis of Fine-tuning with Linear Teachers
Gal Shachaf
Alon Brutzkus
Amir Globerson
31
17
0
04 Jul 2021
The Advantage of Conditional Meta-Learning for Biased Regularization and
  Fine-Tuning
The Advantage of Conditional Meta-Learning for Biased Regularization and Fine-Tuning
Giulia Denevi
Massimiliano Pontil
C. Ciliberto
34
39
0
25 Aug 2020
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn
Pieter Abbeel
Sergey Levine
OOD
338
11,684
0
09 Mar 2017
New Analysis and Algorithm for Learning with Drifting Distributions
New Analysis and Algorithm for Learning with Drifting Distributions
M. Mohri
Andrés Munoz Medina
97
123
0
19 May 2012
1