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Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
v1v2v3 (latest)

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

9 March 2017
Chelsea Finn
Pieter Abbeel
Sergey Levine
    OOD
ArXiv (abs)PDFHTML

Papers citing "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"

50 / 5,503 papers shown
Title
An Algorithmic Perspective on Imitation Learning
An Algorithmic Perspective on Imitation Learning
Takayuki Osa
Joni Pajarinen
Gerhard Neumann
J. Andrew Bagnell
Pieter Abbeel
Jan Peters
100
851
0
16 Nov 2018
Concept Learning through Deep Reinforcement Learning with
  Memory-Augmented Neural Networks
Concept Learning through Deep Reinforcement Learning with Memory-Augmented Neural Networks
Jing Shi
Jiaming Xu
Yiqun Yao
Bo Xu
73
24
0
15 Nov 2018
Machine Learning for Combinatorial Optimization: a Methodological Tour
  d'Horizon
Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon
Yoshua Bengio
Andrea Lodi
Antoine Prouvost
157
1,407
0
15 Nov 2018
Diversity-Driven Extensible Hierarchical Reinforcement Learning
Diversity-Driven Extensible Hierarchical Reinforcement Learning
Yuhang Song
Jianyi Wang
Thomas Lukasiewicz
Zhenghua Xu
Mai Xu
69
18
0
10 Nov 2018
Power Normalizing Second-order Similarity Network for Few-shot Learning
Power Normalizing Second-order Similarity Network for Few-shot Learning
Hongguang Zhang
Piotr Koniusz
75
59
0
10 Nov 2018
Multimodal One-Shot Learning of Speech and Images
Multimodal One-Shot Learning of Speech and Images
Ryan Eloff
H. Engelbrecht
Herman Kamper
SSLVLM
59
35
0
09 Nov 2018
Meta-Learning for Multi-objective Reinforcement Learning
Meta-Learning for Multi-objective Reinforcement Learning
Xi Chen
Ali Ghadirzadeh
Mårten Björkman
Pablo G. Cámara
OffRL
71
56
0
08 Nov 2018
Concept Learning with Energy-Based Models
Concept Learning with Energy-Based Models
William J. Wilkinson
161
26
0
06 Nov 2018
Learning Shared Dynamics with Meta-World Models
Learning Shared Dynamics with Meta-World Models
Lisheng Wu
Minne Li
Jun Wang
32
0
0
05 Nov 2018
Learning to Defend by Learning to Attack
Learning to Defend by Learning to Attack
Haoming Jiang
Zhehui Chen
Yuyang Shi
Bo Dai
T. Zhao
108
22
0
03 Nov 2018
Towards learning-to-learn
Towards learning-to-learn
B. Lansdell
Konrad Paul Kording
43
19
0
01 Nov 2018
Automated Machine Learning: From Principles to Practices
Automated Machine Learning: From Principles to Practices
Quanming Yao
Mengshuo Wang
Hugo Jair Escalante
Huan Zhao
Qiang Yang
121
259
0
31 Oct 2018
Assessing Generalization in Deep Reinforcement Learning
Assessing Generalization in Deep Reinforcement Learning
Charles Packer
Katelyn Gao
Jernej Kos
Philipp Krahenbuhl
V. Koltun
Basel Alomair
OffRL
126
239
0
29 Oct 2018
Learning to Teach with Dynamic Loss Functions
Learning to Teach with Dynamic Loss Functions
Lijun Wu
Fei Tian
Yingce Xia
Yang Fan
Tao Qin
Jianhuang Lai
Tie-Yan Liu
78
112
0
29 Oct 2018
Learning to Learn without Forgetting by Maximizing Transfer and
  Minimizing Interference
Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference
Matthew D Riemer
Ignacio Cases
R. Ajemian
Miao Liu
Irina Rish
Y. Tu
Gerald Tesauro
CLL
109
792
0
29 Oct 2018
Low-shot Learning via Covariance-Preserving Adversarial Augmentation
  Networks
Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks
Hang Gao
Zheng Shou
Alireza Zareian
Hanwang Zhang
Shih-Fu Chang
GAN
95
137
0
27 Oct 2018
The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints
The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints
Andrew Hundt
Varun Jain
Chia-Hung Lin
Chris Paxton
Gregory Hager
83
12
0
27 Oct 2018
One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks
One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks
Tianhe Yu
Pieter Abbeel
Sergey Levine
Chelsea Finn
70
68
0
25 Oct 2018
K for the Price of 1: Parameter-efficient Multi-task and Transfer
  Learning
K for the Price of 1: Parameter-efficient Multi-task and Transfer Learning
Pramod Kaushik Mudrakarta
Mark Sandler
A. Zhmoginov
Andrew G. Howard
82
69
0
25 Oct 2018
Truncated Back-propagation for Bilevel Optimization
Truncated Back-propagation for Bilevel Optimization
Amirreza Shaban
Ching-An Cheng
Nathan Hatch
Byron Boots
121
266
0
25 Oct 2018
FewRel: A Large-Scale Supervised Few-Shot Relation Classification
  Dataset with State-of-the-Art Evaluation
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation
Xu Han
Hao Zhu
Pengfei Yu
Ziyun Wang
Yuan Yao
Zhiyuan Liu
Maosong Sun
SLR
97
616
0
24 Oct 2018
Meta-Learning Multi-task Communication
Meta-Learning Multi-task Communication
Pengfei Liu
Xuanjing Huang
FedML
77
8
0
23 Oct 2018
How to train your MAML
How to train your MAML
Antreas Antoniou
Harrison Edwards
Amos Storkey
97
780
0
22 Oct 2018
SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation
SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation
Xiaolin Zhang
Yunchao Wei
Yi Yang
Thomas Huang
VLM
71
466
0
22 Oct 2018
Zero and Few Shot Learning with Semantic Feature Synthesis and
  Competitive Learning
Zero and Few Shot Learning with Semantic Feature Synthesis and Competitive Learning
Zhiwu Lu
Jiechao Guan
Aoxue Li
Tao Xiang
An Zhao
Ji-Rong Wen
73
64
0
19 Oct 2018
Transferrable Feature and Projection Learning with Class Hierarchy for
  Zero-Shot Learning
Transferrable Feature and Projection Learning with Class Hierarchy for Zero-Shot Learning
Aoxue Li
Zhiwu Lu
Jiechao Guan
Tao Xiang
Liwei Wang
Ji-Rong Wen
VLM
65
20
0
19 Oct 2018
AutoGraph: Imperative-style Coding with Graph-based Performance
AutoGraph: Imperative-style Coding with Graph-based Performance
D. Moldovan
James M. Decker
Fei Wang
A. A. Johnson
Brian K. Lee
Zachary Nado
D. Sculley
Tiark Rompf
Alexander B. Wiltschko
60
43
0
16 Oct 2018
Incremental Few-Shot Learning with Attention Attractor Networks
Incremental Few-Shot Learning with Attention Attractor Networks
Mengye Ren
Renjie Liao
Ethan Fetaya
R. Zemel
CLL
110
182
0
16 Oct 2018
ProMP: Proximal Meta-Policy Search
ProMP: Proximal Meta-Policy Search
Jonas Rothfuss
Dennis Lee
I. Clavera
Tamim Asfour
Pieter Abbeel
96
211
0
16 Oct 2018
Deep Reinforcement Learning
Deep Reinforcement Learning
Yuxi Li
VLMOffRL
194
144
0
15 Oct 2018
Comparison-Based Convolutional Neural Networks for Cervical Cell/Clumps
  Detection in the Limited Data Scenario
Comparison-Based Convolutional Neural Networks for Cervical Cell/Clumps Detection in the Limited Data Scenario
Yixiong Liang
Zhihong Tang
Meng Yan
Jialin Chen
Qing Liu
Yao Xiang
130
11
0
14 Oct 2018
A Data-Efficient Framework for Training and Sim-to-Real Transfer of
  Navigation Policies
A Data-Efficient Framework for Training and Sim-to-Real Transfer of Navigation Policies
Homanga Bharadhwaj
Zihan Wang
Yoshua Bengio
Liam Paull
77
40
0
11 Oct 2018
Fast Context Adaptation via Meta-Learning
Fast Context Adaptation via Meta-Learning
L. Zintgraf
K. Shiarlis
Vitaly Kurin
Katja Hofmann
Shimon Whiteson
88
37
0
08 Oct 2018
Meta-Learning: A Survey
Meta-Learning: A Survey
Joaquin Vanschoren
FedMLOOD
108
761
0
08 Oct 2018
Task-Embedded Control Networks for Few-Shot Imitation Learning
Task-Embedded Control Networks for Few-Shot Imitation Learning
Stephen James
Michael Bloesch
Andrew J. Davison
79
136
0
08 Oct 2018
AutoLoss: Learning Discrete Schedules for Alternate Optimization
AutoLoss: Learning Discrete Schedules for Alternate Optimization
Haowen Xu
Huatian Zhang
Zhiting Hu
Xiaodan Liang
Ruslan Salakhutdinov
Eric Xing
83
30
0
04 Oct 2018
Unsupervised Learning via Meta-Learning
Unsupervised Learning via Meta-Learning
Kyle Hsu
Sergey Levine
Chelsea Finn
SSLOffRL
117
230
0
04 Oct 2018
Learning Scheduling Algorithms for Data Processing Clusters
Learning Scheduling Algorithms for Data Processing Clusters
Hongzi Mao
Malte Schwarzkopf
S. Venkatakrishnan
Zili Meng
Mohammad Alizadeh
OffRL
103
653
0
03 Oct 2018
Set Transformer: A Framework for Attention-based Permutation-Invariant
  Neural Networks
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
Juho Lee
Yoonho Lee
Jungtaek Kim
Adam R. Kosiorek
Seungjin Choi
Yee Whye Teh
140
275
0
01 Oct 2018
Interactive Agent Modeling by Learning to Probe
Interactive Agent Modeling by Learning to Probe
Tianmin Shu
Caiming Xiong
Ying Nian Wu
Song-Chun Zhu
LM&Ro
53
2
0
01 Oct 2018
Few-Shot Goal Inference for Visuomotor Learning and Planning
Few-Shot Goal Inference for Visuomotor Learning and Planning
Annie Xie
Avi Singh
Sergey Levine
Chelsea Finn
OffRL
106
70
0
30 Sep 2018
M$^3$RL: Mind-aware Multi-agent Management Reinforcement Learning
M3^33RL: Mind-aware Multi-agent Management Reinforcement Learning
Tianmin Shu
Yuandong Tian
92
54
0
29 Sep 2018
Generalization and Regularization in DQN
Generalization and Regularization in DQN
Jesse Farebrother
Marlos C. Machado
Michael Bowling
120
208
0
29 Sep 2018
Learning and Planning with a Semantic Model
Learning and Planning with a Semantic Model
Yi Wu
Yuxin Wu
Aviv Tamar
Stuart J. Russell
Georgia Gkioxari
Yuandong Tian
55
17
0
28 Sep 2018
Sample Efficient Adaptive Text-to-Speech
Sample Efficient Adaptive Text-to-Speech
Yutian Chen
Yannis Assael
Brendan Shillingford
David Budden
Scott E. Reed
...
Ben Laurie
Çağlar Gülçehre
Aaron van den Oord
Oriol Vinyals
Nando de Freitas
124
149
0
27 Sep 2018
A Meta-Learning Approach for Custom Model Training
A Meta-Learning Approach for Custom Model Training
Amir Erfan Eshratifar
M. Abrishami
David Eigen
Massoud Pedram
39
6
0
21 Sep 2018
Learning Quickly to Plan Quickly Using Modular Meta-Learning
Learning Quickly to Plan Quickly Using Modular Meta-Learning
Rohan Chitnis
L. Kaelbling
Tomás Lozano-Pérez
76
26
0
20 Sep 2018
The Fast and the Flexible: training neural networks to learn to follow
  instructions from small data
The Fast and the Flexible: training neural networks to learn to follow instructions from small data
Rezka Leonandya
Elia Bruni
Dieuwke Hupkes
Germán Kruszewski
57
6
0
17 Sep 2018
Open-world Learning and Application to Product Classification
Open-world Learning and Application to Product Classification
Hu Xu
Bing-Quan Liu
Lei Shu
P. Yu
CLLVLM
85
111
0
17 Sep 2018
Model-Based Reinforcement Learning via Meta-Policy Optimization
Model-Based Reinforcement Learning via Meta-Policy Optimization
I. Clavera
Jonas Rothfuss
John Schulman
Yasuhiro Fujita
Tamim Asfour
Pieter Abbeel
83
228
0
14 Sep 2018
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