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Global Convergence of MAML and Theory-Inspired Neural Architecture
  Search for Few-Shot Learning

Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot Learning

17 March 2022
Haoxiang Wang
Yite Wang
Ruoyu Sun
Yue Liu
ArXivPDFHTML

Papers citing "Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot Learning"

21 / 21 papers shown
Title
Zero-Cost Proxies for Lightweight NAS
Zero-Cost Proxies for Lightweight NAS
Mohamed S. Abdelfattah
Abhinav Mehrotra
Łukasz Dudziak
Nicholas D. Lane
60
257
0
20 Jan 2021
Neural Architecture Search without Training
Neural Architecture Search without Training
J. Mellor
Jack Turner
Amos Storkey
Elliot J. Crowley
51
380
0
08 Jun 2020
Meta-Learning in Neural Networks: A Survey
Meta-Learning in Neural Networks: A Survey
Timothy M. Hospedales
Antreas Antoniou
P. Micaelli
Amos Storkey
OOD
340
1,967
0
11 Apr 2020
Theoretical Convergence of Multi-Step Model-Agnostic Meta-Learning
Theoretical Convergence of Multi-Step Model-Agnostic Meta-Learning
Kaiyi Ji
Junjie Yang
Yingbin Liang
75
50
0
18 Feb 2020
Meta-Learning of Neural Architectures for Few-Shot Learning
Meta-Learning of Neural Architectures for Few-Shot Learning
T. Elsken
B. Staffler
J. H. Metzen
Frank Hutter
73
137
0
25 Nov 2019
Learning to Few-Shot Learn Across Diverse Natural Language
  Classification Tasks
Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks
Trapit Bansal
Rishikesh Jha
Andrew McCallum
SSL
53
119
0
10 Nov 2019
Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness
  of MAML
Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML
Aniruddh Raghu
M. Raghu
Samy Bengio
Oriol Vinyals
300
644
0
19 Sep 2019
Meta-Learning with Implicit Gradients
Meta-Learning with Implicit Gradients
Aravind Rajeswaran
Chelsea Finn
Sham Kakade
Sergey Levine
94
854
0
10 Sep 2019
On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning
  Algorithms
On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms
Alireza Fallah
Aryan Mokhtari
Asuman Ozdaglar
75
225
0
27 Aug 2019
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep
  Neural Networks
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks
Yuan Cao
Quanquan Gu
MLT
AI4CE
80
389
0
30 May 2019
Meta-Learning: A Survey
Meta-Learning: A Survey
Joaquin Vanschoren
FedML
OOD
60
763
0
08 Oct 2018
DARTS: Differentiable Architecture Search
DARTS: Differentiable Architecture Search
Hanxiao Liu
Karen Simonyan
Yiming Yang
185
4,345
0
24 Jun 2018
Auto-Meta: Automated Gradient Based Meta Learner Search
Auto-Meta: Automated Gradient Based Meta Learner Search
Jaehong Kim
Sangyeul Lee
Sungwan Kim
Moonsu Cha
Jung Kwon Lee
Youngduck Choi
Yongseok Choi
Dong-Yeon Cho
Jiwon Kim
AI4CE
71
39
0
11 Jun 2018
Probabilistic Model-Agnostic Meta-Learning
Probabilistic Model-Agnostic Meta-Learning
Chelsea Finn
Kelvin Xu
Sergey Levine
BDL
255
670
0
07 Jun 2018
Meta-learning with differentiable closed-form solvers
Meta-learning with differentiable closed-form solvers
Luca Bertinetto
João F. Henriques
Philip Torr
Andrea Vedaldi
ODL
80
928
0
21 May 2018
Group Normalization
Group Normalization
Yuxin Wu
Kaiming He
190
3,644
0
22 Mar 2018
On First-Order Meta-Learning Algorithms
On First-Order Meta-Learning Algorithms
Alex Nichol
Joshua Achiam
John Schulman
221
2,229
0
08 Mar 2018
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
803
11,866
0
09 Mar 2017
Neural Architecture Search with Reinforcement Learning
Neural Architecture Search with Reinforcement Learning
Barret Zoph
Quoc V. Le
416
5,367
0
05 Nov 2016
Matching Networks for One Shot Learning
Matching Networks for One Shot Learning
Oriol Vinyals
Charles Blundell
Timothy Lillicrap
Koray Kavukcuoglu
Daan Wierstra
VLM
331
7,313
0
13 Jun 2016
Wide Residual Networks
Wide Residual Networks
Sergey Zagoruyko
N. Komodakis
304
7,971
0
23 May 2016
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