Paper List¶
The following papers were announced on the learn2learn Twitter account. You can submit unannounced and meta-learning related papers through the following Google Form. (It does not matter if they are old or new, but they shouldn't be already announced.)
Info
Announce any paper via the Google Form to announce papers, also available below.
Submitted Papers¶
Grounded Language Learning Fast and Slow
by Hill, Felix and Tieleman, Olivier and von Glehn, Tamara and
Wong, Nathaniel and Merzic, Hamza and Clark, Stephen
http://arxiv.org/abs/2009.01719
Sparse Meta Networks for Sequential Adaptation and its
Application to Adaptive Language Modelling
by Munkhdalai, Tsendsuren
http://arxiv.org/abs/2009.01803
Learning with Differentiable Perturbed Optimizers
by Berthet, Quentin and Blondel, Mathieu and Teboul, Olivier
and Cuturi, Marco and Vert, Jean-Philippe and Bach, Francis
http://arxiv.org/abs/2002.08676
What is being transferred in transfer learning?
by Neyshabur, Behnam and Sedghi, Hanie and Zhang, Chiyuan
http://arxiv.org/abs/2008.11687
On modulating the gradient for meta-learning
by Simon, Christian and Koniusz, Piotr and Nock, Richard and
Harandi, Mehrtash
https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123530545.pdf
Meta-Learning with Shared Amortized Variational Inference
by Iakovleva, Ekaterina and Verbeek, Jakob and Alahari, Karteek
http://arxiv.org/abs/2008.12037
learn2learn: A Library for Meta-Learning Research
by Arnold, Sébastien M R and Mahajan, Praateek and Datta,
Debajyoti and Bunner, Ian and Zarkias, Konstantinos Saitas
http://arxiv.org/abs/2008.12284
A Universal Representation Transformer Layer for Few-Shot
Image Classification
by Liu, Lu and Hamilton, William and Long, Guodong and Jiang,
Jing and Larochelle, Hugo
http://arxiv.org/abs/2006.11702
Safe Model-Based Meta-Reinforcement Learning: A
Sequential Exploration-Exploitation Framework
by Lew, Thomas and Sharma, Apoorva and Harrison, James and
Pavone, Marco
http://arxiv.org/abs/2008.11700
Learning to Learn in a Semi-Supervised Fashion
by Chen, Yun-Chun and Chou, Chao-Te and Wang, Yu-Chiang Frank
http://arxiv.org/abs/2008.11203
The Advantage of Conditional Meta-Learning for Biased
Regularization and Fine-Tuning
by Denevi, Giulia and Pontil, Massimiliano and Ciliberto, Carlo
http://arxiv.org/abs/2008.10857
Adaptive Multi-level Hyper-gradient Descent
by Jie, Renlong and Gao, Junbin and Vasnev, Andrey and Tran,
Minh-Ngoc
http://arxiv.org/abs/2008.07277
Few-Shot Image Classification via Contrastive
Self-Supervised Learning
by Li, Jianyi and Liu, Guizhong
http://arxiv.org/abs/2008.09942
Does MAML really want feature reuse only?
by Oh, Jaehoon and Yoo, Hyungjun and Kim, Changhwan and Yun,
Se-Young
http://arxiv.org/abs/2008.08882
Meta Learning MPC using Finite-Dimensional Gaussian
Process Approximations
by Arcari, Elena and Carron, Andrea and Zeilinger, Melanie N
http://arxiv.org/abs/2008.05984
Offline Meta-Reinforcement Learning with Advantage
Weighting
by Mitchell, Eric and Rafailov, Rafael and Peng, Xue Bin and
Levine, Sergey and Finn, Chelsea
http://arxiv.org/abs/2008.06043
Explore then Execute: Adapting without Rewards via
Factorized Meta-Reinforcement Learning
by Liu, Evan Zheran and Raghunathan, Aditi and Liang, Percy and
Finn, Chelsea
http://arxiv.org/abs/2008.02790
Offline Meta Reinforcement Learning
by Dorfman, Ron and Tamar, Aviv
http://arxiv.org/abs/2008.02598
Few-Shot Learning via Learning the Representation,
Provably
by Du, Simon S and Hu, Wei and Kakade, Sham M and Lee, Jason D
and Lei, Qi
http://arxiv.org/abs/2002.09434
Multi-Task Reinforcement Learning as a Hidden-Parameter
Block MDP
by Zhang, Amy and Sodhani, Shagun and Khetarpal, Khimya and
Pineau, Joelle
http://arxiv.org/abs/2007.07206
CAMPs: Learning Context-Specific Abstractions for
Efficient Planning in Factored MDPs
by Chitnis, Rohan and Silver, Tom and Kim, Beomjoon and
Kaelbling, Leslie Pack and Lozano-Perez, Tomas
http://arxiv.org/abs/2007.13202
Unsupervised Learning of Visual Features by Contrasting
Cluster Assignments
by Caron, Mathilde and Misra, Ishan and Mairal, Julien and
Goyal, Priya and Bojanowski, Piotr and Joulin, Armand
http://arxiv.org/abs/2006.09882
MiCo: Mixup Co-Training for Semi-Supervised Domain
Adaptation
by Yang, Luyu and Wang, Yan and Gao, Mingfei and Shrivastava,
Abhinav and Weinberger, Kilian Q and Chao, Wei-Lun and Lim,
Ser-Nam
http://arxiv.org/abs/2007.12684
Adaptive Task Sampling for Meta-Learning
by Liu, Chenghao and Wang, Zhihao and Sahoo, Doyen and Fang,
Yuan and Zhang, Kun and Hoi, Steven C H
http://arxiv.org/abs/2007.08735
Discovering Reinforcement Learning Algorithms
by Oh, Junhyuk and Hessel, Matteo and Czarnecki, Wojciech M and
Xu, Zhongwen and van Hasselt, Hado and Singh, Satinder and
Silver, David
http://arxiv.org/abs/2007.08794
On the Outsized Importance of Learning Rates in Local Update
Methods
by Charles, Zachary and Kone{\v c}n{\'y}, Jakub
http://arxiv.org/abs/2007.00878
Global Convergence and Induced Kernels of Gradient-Based
Meta-Learning with Neural Nets
by Wang, Haoxiang and Sun, Ruoyu and Li, Bo
http://arxiv.org/abs/2006.14606
On the Iteration Complexity of Hypergradient Computation
by Grazzi, Riccardo and Franceschi, Luca and Pontil,
Massimiliano and Salzo, Saverio
http://arxiv.org/abs/2006.16218
On the Outsized Importance of Learning Rates in Local Update
Methods
by Charles, Zachary and Kone{\v c}n{\'y}, Jakub
http://arxiv.org/abs/2007.00878
Meta-SAC: Auto-tune the Entropy Temperature of Soft
Actor-Critic via Metagradient
by Wang, Yufei and Ni, Tianwei
http://arxiv.org/abs/2007.01932
Meta Learning in the Continuous Time Limit
by Xu, Ruitu and Chen, Lin and Karbasi, Amin
http://arxiv.org/abs/2006.10921
Expert Training: Task Hardness Aware Meta-Learning for
Few-Shot Classification
by Zhou, Yucan and Wang, Yu and Cai, Jianfei and Zhou, Yu and
Hu, Qinghua and Wang, Weiping
http://arxiv.org/abs/2007.06240
MTL2L: A Context Aware Neural Optimiser
by Kuo, Nicholas I-Hsien and Harandi, Mehrtash and Fourrier,
Nicolas and Walder, Christian and Ferraro, Gabriela and
Suominen, Hanna
http://arxiv.org/abs/2007.09343
Navigating the Trade-Off between Multi-Task Learning and
Learning to Multitask in Deep Neural Networks
by Ravi, Sachin and Musslick, Sebastian and Hamin, Maia and
Willke, Theodore L and Cohen, Jonathan D
http://arxiv.org/abs/2007.10527
Balanced Meta-Softmax for Long-Tailed Visual Recognition
by Ren, Jiawei and Yu, Cunjun and Sheng, Shunan and Ma, Xiao
and Zhao, Haiyu and Yi, Shuai and Li, Hongsheng
http://arxiv.org/abs/2007.10740
CrossTransformers: spatially-aware few-shot transfer
by Doersch, Carl and Gupta, Ankush and Zisserman, Andrew
http://arxiv.org/abs/2007.11498
Meta-Learning a Dynamical Language Model
by Wolf, Thomas and Chaumond, Julien and Delangue, Clement
http://arxiv.org/abs/1803.10631
Meta-Learning Requires Meta-Augmentation
by Rajendran, Janarthanan and Irpan, Alex and Jang, Eric
http://arxiv.org/abs/2007.05549
Adaptive Risk Minimization: A Meta-Learning Approach for
Tackling Group Shift
by Zhang, Marvin and Marklund, Henrik and Gupta, Abhishek and
Levine, Sergey and Finn, Chelsea
http://arxiv.org/abs/2007.02931
Meta-Learning Symmetries by Reparameterization
by Zhou, Allan and Knowles, Tom and Finn, Chelsea
http://arxiv.org/abs/2007.02933
Adaptive Risk Minimization: A Meta-Learning Approach for
Tackling Group Shift
by Zhang, Marvin and Marklund, Henrik and Gupta, Abhishek and
Levine, Sergey and Finn, Chelsea
http://arxiv.org/abs/2007.02931
A Brief Look at Generalization in Visual
Meta-Reinforcement Learning
by Alver, Safa and Precup, Doina
http://arxiv.org/abs/2006.07262
Learning Representations by Stochastic Meta-Gradient
Descent in Neural Networks
by Veeriah, Vivek and Zhang, Shangtong and Sutton, Richard S
http://arxiv.org/abs/1612.02879
PACOH: Bayes-Optimal Meta-Learning with
PAC-Guarantees
by Rothfuss, Jonas and Fortuin, Vincent and Krause, Andreas
http://arxiv.org/abs/2002.05551
Meta-Meta-Classification for One-Shot Learning
by Chowdhury, Arkabandhu and Chaudhari, Dipak and Chaudhuri,
Swarat and Jermaine, Chris
http://arxiv.org/abs/2004.08083
Relatedness Measures to Aid the Transfer of Building Blocks
among Multiple Tasks
by Nguyen, Trung B and Browne, Will N and Zhang, Mengjie
http://arxiv.org/abs/2005.03947
Information-Theoretic Generalization Bounds for
Meta-Learning and Applications
by Jose, Sharu Theresa and Simeone, Osvaldo
http://arxiv.org/abs/2005.04372
On Learning Intrinsic Rewards for Policy Gradient Methods
by Zheng, Zeyu and Oh, Junhyuk and Singh, Satinder
http://arxiv.org/abs/1804.06459
A Sample Complexity Separation between Non-Convex and
Convex Meta-Learning
by Saunshi, Nikunj and Zhang, Yi and Khodak, Mikhail and Arora,
Sanjeev
http://arxiv.org/abs/2002.11172
Bayesian Online Meta-Learning with Laplace Approximation
by Yap, Pau Ching and Ritter, Hippolyt and Barber, David
http://arxiv.org/abs/2005.00146
Meta-Reinforcement Learning for Robotic Industrial
Insertion Tasks
by Schoettler, Gerrit and Nair, Ashvin and Ojea, Juan Aparicio
and Levine, Sergey and Solowjow, Eugen
http://arxiv.org/abs/2004.14404
Continual Deep Learning by Functional Regularisation of
Memorable Past
by Pan, Pingbo and Swaroop, Siddharth and Immer, Alexander and
Eschenhagen, Runa and Turner, Richard E and Khan, Mohammad
Emtiyaz
http://arxiv.org/abs/2004.14070
Jelly Bean World: A Testbed for Never-Ending Learning
by Platanios, Emmanouil Antonios and Saparov, Abulhair and Mitchell,
Tom
https://openreview.net/pdf?id=Byx_YAVYPH
Encouraging behavioral diversity in evolutionary robotics: an
empirical study
by Mouret, J-B and Doncieux, S
http://dx.doi.org/10.1162/EVCO_a_00048
Defining Benchmarks for Continual Few-Shot Learning
by Antoniou, Antreas and Patacchiola, Massimiliano and Ochal,
Mateusz and Storkey, Amos
http://arxiv.org/abs/2004.11967
Emergent Real-World Robotic Skills via Unsupervised
Off-Policy Reinforcement Learning
by Sharma, Archit and Ahn, Michael and Levine, Sergey and
Kumar, Vikash and Hausman, Karol and Gu, Shixiang
http://arxiv.org/abs/2004.12974
Empirical Bayes Transductive Meta-Learning with Synthetic
Gradients
by Hu, Shell Xu and Moreno, Pablo G and Xiao, Yang and Shen, Xi
and Obozinski, Guillaume and Lawrence, Neil D and Damianou,
Andreas
http://arxiv.org/abs/2004.12696
Evolving Inborn Knowledge For Fast Adaptation in Dynamic
POMDP Problems
by Ben-Iwhiwhu, Eseoghene and Ladosz, Pawel and Dick, Jeffery
and Chen, Wen-Hua and Pilly, Praveen and Soltoggio, Andrea
http://arxiv.org/abs/2004.12846
Meta-World: A Benchmark and Evaluation for Multi-Task
and Meta Reinforcement Learning
by Yu, Tianhe and Quillen, Deirdre and He, Zhanpeng and Julian,
Ryan and Hausman, Karol and Finn, Chelsea and Levine, Sergey
http://arxiv.org/abs/1910.10897
Meta reinforcement learning as task inference
by Humplik, Jan and Galashov, Alexandre and Hasenclever,
Leonard and Ortega, Pedro A and Teh, Yee Whye and Heess,
Nicolas
http://arxiv.org/abs/1905.06424
Meta-Gradient Reinforcement Learning
by Xu, Zhongwen and van Hasselt, Hado and Silver, David
http://arxiv.org/abs/1805.09801
Self-Paced Deep Reinforcement Learning
by Klink, Pascal and D'Eramo, Carlo and Peters, Jan and
Pajarinen, Joni
http://arxiv.org/abs/2004.11812
Scheduling the Learning Rate Via Hypergradients: New Insights and
a New Algorithm
by Donini, Michele and Franceschi, Luca and Majumder, Orchid and
Pontil, Massimiliano and Frasconi, Paolo
https://openreview.net/pdf?id=Ske6qJSKPH
Learning Stabilizable Nonlinear Dynamics with
Contraction-Based Regularization
by Singh, Sumeet and Richards, Spencer M and Sindhwani, Vikas
and Slotine, Jean-Jacques E and Pavone, Marco
http://arxiv.org/abs/1907.13122
A Comprehensive Overview and Survey of Recent Advances in
Meta-Learning
by Peng, Huimin
http://arxiv.org/abs/2004.11149
Learning a Formula of Interpretability to Learn
Interpretable Formulas
by Virgolin, Marco and De Lorenzo, Andrea and Medvet, Eric and
Randone, Francesca
http://arxiv.org/abs/2004.11170
Model-Based Meta-Reinforcement Learning for Flight with
Suspended Payloads
by Belkhale, Suneel and Li, Rachel and Kahn, Gregory and
McAllister, Rowan and Calandra, Roberto and Levine, Sergey
http://arxiv.org/abs/2004.11345
Frustratingly Simple Few-Shot Object Detection
by Wang, Xin and Huang, Thomas E and Darrell, Trevor and
Gonzalez, Joseph E and Yu, Fisher
http://arxiv.org/abs/2003.06957
Meta Pseudo Labels
by Pham, Hieu and Xie, Qizhe and Dai, Zihang and Le, Quoc V
http://arxiv.org/abs/2003.10580
0e56da12-a2f0-4288-b745-c15deec9183a
by Unknown
http://learn2learn.net
Finding online neural update rules by learning to remember
by Gregor, Karol
http://arxiv.org/abs/2003.03124
A New Meta-Baseline for Few-Shot Learning
by Chen, Yinbo and Wang, Xiaolong and Liu, Zhuang and Xu,
Huijuan and Darrell, Trevor
http://arxiv.org/abs/2003.04390
Learning to be Global Optimizer
by Zhang, Haotian and Sun, Jianyong and Xu, Zongben
http://arxiv.org/abs/2003.04521
Scalable Multi-Task Imitation Learning with Autonomous
Improvement
by Singh, Avi and Jang, Eric and Irpan, Alexander and Kappler,
Daniel and Dalal, Murtaza and Levine, Sergey and Khansari,
Mohi and Finn, Chelsea
http://arxiv.org/abs/2003.02636
Meta-learning for mixed linear regression
by Kong, Weihao and Somani, Raghav and Song, Zhao and Kakade,
Sham and Oh, Sewoong
http://arxiv.org/abs/2002.08936
Provable Meta-Learning of Linear Representations
by Tripuraneni, Nilesh and Jin, Chi and Jordan, Michael I
http://arxiv.org/abs/2002.11684
Learning to Continually Learn
by Beaulieu, Shawn and Frati, Lapo and Miconi, Thomas and
Lehman, Joel and Stanley, Kenneth O and Clune, Jeff and
Cheney, Nick
http://arxiv.org/abs/2002.09571
PACOH: Bayes-Optimal Meta-Learning with
PAC-Guarantees
by Rothfuss, Jonas and Fortuin, Vincent and Krause, Andreas
http://arxiv.org/abs/2002.05551
Incremental Learning for Metric-Based Meta-Learners
by Liu, Qing and Majumder, Orchid and Ravichandran, Avinash and
Bhotika, Rahul and Soatto, Stefano
http://arxiv.org/abs/2002.04162
Hyper-Meta Reinforcement Learning with Sparse Reward
by Hua, Yun and Wang, Xiangfeng and Jin, Bo and Li, Wenhao and
Yan, Junchi and He, Xiaofeng and Zha, Hongyuan
http://arxiv.org/abs/2002.04238
Meta-Learning across Meta-Tasks for Few-Shot Learning
by Fei, Nanyi and Lu, Zhiwu and Gao, Yizhao and Tian, Jia and
Xiang, Tao and Wen, Ji-Rong
http://arxiv.org/abs/2002.04274
Distribution-Agnostic Model-Agnostic Meta-Learning
by Collins, Liam and Mokhtari, Aryan and Shakkottai, Sanjay
http://arxiv.org/abs/2002.04766
Provably Convergent Policy Gradient Methods for
Model-Agnostic Meta-Reinforcement Learning
by Fallah, Alireza and Mokhtari, Aryan and Ozdaglar, Asuman
http://arxiv.org/abs/2002.05135
Meta-learning framework with applications to zero-shot
time-series forecasting
by Oreshkin, Boris N and Carpov, Dmitri and Chapados, Nicolas
and Bengio, Yoshua
http://arxiv.org/abs/2002.02887
A Loss-Function for Causal Machine-Learning
by Yang, I-Sheng
http://arxiv.org/abs/2001.00629
Self-Tuning Deep Reinforcement Learning
by Zahavy, Tom and Xu, Zhongwen and Veeriah, Vivek and Hessel,
Matteo and Van Hasslet, Hado and Silver, David and Singh,
Satinder
http://arxiv.org/abs/2002.12928
Learning Adaptive Loss for Robust Learning with Noisy Labels
by Shu, Jun and Zhao, Qian and Chen, Keyu and Xu, Zongben and
Meng, Deyu
http://arxiv.org/abs/2002.06482
A Structured Prediction Approach for Conditional
Meta-Learning
by Wang, Ruohan and Demiris, Yiannis and Ciliberto, Carlo
http://arxiv.org/abs/2002.08799
Curriculum in Gradient-Based Meta-Reinforcement Learning
by Mehta, Bhairav and Deleu, Tristan and Raparthy, Sharath
Chandra and Pal, Chris J and Paull, Liam
http://arxiv.org/abs/2002.07956
Multi-Step Model-Agnostic Meta-Learning: Convergence
and Improved Algorithms
by Ji, Kaiyi and Yang, Junjie and Liang, Yingbin
http://arxiv.org/abs/2002.07836
Local Nonparametric Meta-Learning
by Goo, Wonjoon and Niekum, Scott
http://arxiv.org/abs/2002.03272
Revisiting Meta-Learning as Supervised Learning
by Chao, Wei-Lun and Ye, Han-Jia and Zhan, De-Chuan and
Campbell, Mark and Weinberger, Kilian Q
http://arxiv.org/abs/2002.00573
SimpleShot: Revisiting Nearest-Neighbor Classification
for Few-Shot Learning
by Wang, Yan and Chao, Wei-Lun and Weinberger, Kilian Q and van
der Maaten, Laurens
http://arxiv.org/abs/1911.04623
Fast and Generalized Adaptation for Few-Shot Learning
by Song, Liang and Liu, Jinlu and Qin, Yongqiang
http://arxiv.org/abs/1911.10807
Meta-Learning without Memorization
by Yin, Mingzhang and Tucker, George and Zhou, Mingyuan and
Levine, Sergey and Finn, Chelsea
http://arxiv.org/abs/1912.03820
Your Classifier is Secretly an Energy Based Model and You
Should Treat it Like One
by Grathwohl, Will and Wang, Kuan-Chieh and Jacobsen,
J{\"o}rn-Henrik and Duvenaud, David and Norouzi, Mohammad
and Swersky, Kevin
http://arxiv.org/abs/1912.03263
MAME : Model-Agnostic Meta-Exploration
by Gurumurthy, Swaminathan and Kumar, Sumit and Sycara, Katia
http://arxiv.org/abs/1911.04024
Constructing Multiple Tasks for Augmentation: Improving
Neural Image Classification With K-means Features
by Gui, Tao and Qing, Lizhi and Zhang, Qi and Ye, Jiacheng and
Yan, Hang and Fei, Zichu and Huang, Xuanjing
http://arxiv.org/abs/1911.07518
Meta Adaptation using Importance Weighted Demonstrations
by Lekkala, Kiran and Abu-El-Haija, Sami and Itti, Laurent
http://arxiv.org/abs/1911.10322
VIABLE: Fast Adaptation via Backpropagating Learned Loss
by Feng, Leo and Zintgraf, Luisa and Peng, Bei and Whiteson,
Shimon
http://arxiv.org/abs/1911.13159
Decoupling Adaptation from Modeling with Meta-Optimizers
for Meta Learning
by Arnold, S{\'e}bastien M R and Iqbal, Shariq and Sha, Fei
http://arxiv.org/abs/1910.13603
TADAM: Task dependent adaptive metric for improved few-shot
learning
by Oreshkin, Boris and Rodr{\'\i}guez L{\'o}pez, Pau and Lacoste,
Alexandre
http://papers.nips.cc/paper/7352-tadam-task-dependent-adaptive-metric-for-improved-few-shot-learning.pdf
Learning to Few-Shot Learn Across Diverse Natural Language
Classification Tasks
by Bansal, Trapit and Jha, Rishikesh and McCallum, Andrew
http://arxiv.org/abs/1911.03863
Optimizing Millions of Hyperparameters by Implicit
Differentiation
by Lorraine, Jonathan and Vicol, Paul and Duvenaud, David
http://arxiv.org/abs/1911.02590
Meta-data: Characterization of Input Features for Meta-learning
by Castiello, Ciro and Castellano, Giovanna and Fanelli, Anna Maria
http://dx.doi.org/10.1007/11526018_45
Meta-Learning for Low-resource Natural Language Generation
in Task-oriented Dialogue Systems
by Mi, Fei and Huang, Minlie and Zhang, Jiyong and Faltings,
Boi
http://arxiv.org/abs/1905.05644
Domain Generalization via Model-Agnostic Learning of
Semantic Features
by Dou, Qi and Castro, Daniel C and Kamnitsas, Konstantinos and
Glocker, Ben
http://arxiv.org/abs/1910.13580
Hierarchical Expert Networks for Meta-Learning
by Hihn, Heinke and Braun, Daniel A
http://arxiv.org/abs/1911.00348
Online Meta-Learning on Non-convex Setting
by Zhuang, Zhenxun and Wang, Yunlong and Yu, Kezi and Lu,
Songtao
http://arxiv.org/abs/1910.10196
Learning-to-Learn Stochastic Gradient Descent with Biased
Regularization
by Denevi, Giulia and Ciliberto, Carlo and Grazzi, Riccardo and
Pontil, Massimiliano
http://arxiv.org/abs/1903.10399
Provable Guarantees for Gradient-Based Meta-Learning
by Khodak, Mikhail and Balcan, Maria-Florina and Talwalkar,
Ameet
http://arxiv.org/abs/1902.10644
The TCGA Meta-Dataset Clinical Benchmark
by Samiei, Mandana and W{\"u}rfl, Tobias and Deleu, Tristan and
Weiss, Martin and Dutil, Francis and Fevens, Thomas and
Boucher, Genevi{`e}ve and Lemieux, Sebastien and Cohen,
Joseph Paul
http://arxiv.org/abs/1910.08636
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL
via Meta-Learning
by Zintgraf, Luisa and Shiarlis, Kyriacos and Igl, Maximilian
and Schulze, Sebastian and Gal, Yarin and Hofmann, Katja and
Whiteson, Shimon
http://arxiv.org/abs/1910.08348
Meta-Transfer Learning through Hard Tasks
by Sun, Qianru and Liu, Yaoyao and Chen, Zhaozheng and Chua,
Tat-Seng and Schiele, Bernt
http://arxiv.org/abs/1910.03648
Model-Agnostic Meta-Learning using Runge-Kutta Methods
by Im, Daniel Jiwoong and Jiang, Yibo and Verma, Nakul
http://arxiv.org/abs/1910.07368
Improving Generalization in Meta Reinforcement Learning
using Learned Objectives
by Kirsch, Louis and van Steenkiste, Sjoerd and Schmidhuber,
J{\"u}rgen
http://arxiv.org/abs/1910.04098
Generalized Inner Loop Meta-Learning
by Grefenstette, Edward and Amos, Brandon and Yarats, Denis and
Htut, Phu Mon and Molchanov, Artem and Meier, Franziska and
Kiela, Douwe and Cho, Kyunghyun and Chintala, Soumith
http://arxiv.org/abs/1910.01727
Is Fast Adaptation All You Need?
by Javed, Khurram and Yao, Hengshuai and White, Martha
http://arxiv.org/abs/1910.01705
Deep Reinforcement Learning for Single-Shot Diagnosis and
Adaptation in Damaged Robots
by Verma, Shresth and Nair, Haritha S and Agarwal, Gaurav and
Dhar, Joydip and Shukla, Anupam
http://arxiv.org/abs/1910.01240
ES-MAML: Simple Hessian-Free Meta Learning
by Song, Xingyou and Gao, Wenbo and Yang, Yuxiang and
Choromanski, Krzysztof and Pacchiano, Aldo and Tang, Yunhao
http://arxiv.org/abs/1910.01215
Meta-Q-Learning
by Fakoor, Rasool and Chaudhari, Pratik and Soatto, Stefano and
Smola, Alexander J
http://arxiv.org/abs/1910.00125
Efficient meta reinforcement learning via meta goal
generation
by Fu, Haotian and Tang, Hongyao and Hao, Jianye
http://arxiv.org/abs/1909.13607
Chameleon: Learning Model Initializations Across Tasks With
Different Schemas
by Brinkmeyer, Lukas and Drumond, Rafael Rego and Scholz,
Randolf and Grabocka, Josif and Schmidt-Thieme, Lars
http://arxiv.org/abs/1909.13576
Learning Fast Adaptation with Meta Strategy Optimization
by Yu, Wenhao and Tan, Jie and Bai, Yunfei and Coumans, Erwin
and Ha, Sehoon
http://arxiv.org/abs/1909.12995
Meta-Inverse Reinforcement Learning with Probabilistic
Context Variables
by Yu, Lantao and Yu, Tianhe and Finn, Chelsea and Ermon,
Stefano
http://arxiv.org/abs/1909.09314
Modular Meta-Learning with Shrinkage
by Chen, Yutian and Friesen, Abram L and Behbahani, Feryal and
Budden, David and Hoffman, Matthew W and Doucet, Arnaud and
de Freitas, Nando
http://arxiv.org/abs/1909.05557
Loaded DiCE: Trading off Bias and Variance in Any-Order
Score Function Estimators for Reinforcement Learning
by Farquhar, Gregory and Whiteson, Shimon and Foerster, Jakob
http://arxiv.org/abs/1909.10549
Rapid Learning or Feature Reuse? Towards Understanding the
Effectiveness of MAML
by Raghu, Aniruddh and Raghu, Maithra and Bengio, Samy and
Vinyals, Oriol
http://arxiv.org/abs/1909.09157
Meta-Learning
by Vanschoren, Joaquin
https://doi.org/10.1007/978-3-030-05318-5_2
Understanding Short-Horizon Bias in Stochastic
Meta-Optimization
by Wu, Yuhuai and Ren, Mengye and Liao, Renjie and Grosse,
Roger
http://arxiv.org/abs/1803.02021
On First-Order Meta-Learning Algorithms
by Nichol, Alex and Achiam, Joshua and Schulman, John
http://arxiv.org/abs/1803.02999
Towards Understanding Generalization in Gradient-Based
Meta-Learning
by Guiroy, Simon and Verma, Vikas and Pal, Christopher
http://arxiv.org/abs/1907.07287
They empirically study the landscape of fast-adaptation in
MAML. The most interesting claim is that when
meta-overfitting, the loss landscape becomes flatter on test
tasks.
On the Convergence Theory of Gradient-Based
Model-Agnostic Meta-Learning Algorithms
by Fallah, Alireza and Mokhtari, Aryan and Ozdaglar, Asuman
http://arxiv.org/abs/1908.10400
Learning to Learn with Gradients
by Finn, Chelsea
http://learn2learn.net
Acetylcholine and memory
by Hasselmo, M E and Bower, J M
https://www.ncbi.nlm.nih.gov/pubmed/7688162
A THEORY OF META-LEARNING AND PRINCIPLES OF
FACILITATION: AN ORGANISMIC PERSPECTIVE
by Maudsley, Donald B
https://uosc.primo.exlibrisgroup.com/discovery/fulldisplay?docid=proquest302999651&context=PC&vid=01USC_INST:01USC&lang=en&search_scope=MyInst_and_CI&adaptor=Primo%20Central&tab=Everything&mode=Basic
THE ROLE OF METALEARNING IN STUDY PROCESSES
by Biggs, J B
http://doi.wiley.com/10.1111/j.2044-8279.1985.tb02625.x
Understanding and correcting pathologies in the training of
learned optimizers
by Metz, Luke and Maheswaranathan, Niru and Nixon, Jeremy and
Daniel Freeman, C and Sohl-Dickstein, Jascha
http://arxiv.org/abs/1810.10180
Provides many tricks (e.g. split train batch for model \&
opt, average gradient estimators) for training
differentiable optimizers online. They also have a couple of
interesting observations specific to recurrent optimizers.
Learned Optimizers that Scale and Generalize
by Wichrowska, Olga and Maheswaranathan, Niru and Hoffman,
Matthew W and Colmenarejo, Sergio Gomez and Denil, Misha and
de Freitas, Nando and Sohl-Dickstein, Jascha
http://arxiv.org/abs/1703.04813
Using learned optimizers to make models robust to input
noise
by Metz, Luke and Maheswaranathan, Niru and Shlens, Jonathon
and Sohl-Dickstein, Jascha and Cubuk, Ekin D
http://arxiv.org/abs/1906.03367
Learning to Optimize Neural Nets
by Li, Ke and Malik, Jitendra
http://arxiv.org/abs/1703.00441
Meta-Learning Update Rules for Unsupervised Representation
Learning
by Metz, Luke and Maheswaranathan, Niru and Cheung, Brian and
Sohl-Dickstein, Jascha
http://arxiv.org/abs/1804.00222
Learning to Optimize
by Li, Ke and Malik, Jitendra
http://arxiv.org/abs/1606.01885
Learning to learn by gradient descent by gradient descent
by Andrychowicz, M and Denil, M and Gomez, S
http://learn2learn.net
Online Learning Rate Adaptation with Hypergradient Descent
by Baydin, Atilim Gunes and Cornish, Robert and Rubio, David
Martinez and Schmidt, Mark and Wood, Frank
http://arxiv.org/abs/1703.04782
They adapt the learning rate of SGD by differentiating
the loss of the next parameters w.r.t. the learning
rate. They observe
that the gradient of the learning rate is simply the inner
product of the last two
gradients.
Adapting Bias by Gradient Descent: An Incremental Version of
Delta-Bar-Delta
by Sutton, Richard S
http://dx.doi.org/
What's mostly interesting in this paper is the adaptation of
delta-bar-delta to the online scenario. The idea of representing
the learning rate as an exponential is nice. Also nice to see
that the derivation suggests a full-matrix adaptive case.
Gain adaptation beats least squares
by Sutton, Richard S
https://pdfs.semanticscholar.org/7ec8/876f219b3b3d5c894a3f395c89c382029cc5.pdf
This paper extends IDBD as algorithms K1 and K2, but from my
quick read, it isn't clear what's the motivation for those
modifications. (Seems to work in a `normalized space'', {\a}
la natural gradient ?)They do work better.
Local Gain Adaptation in Stochastic Gradient Descent
by Schraudolph, Nicol N
https://pdfs.semanticscholar.org/31a0/b86c3cd04e6539626f34b80db7ff79d23f40.pdf
This algorithm extends IDBD (Sutton) to the non-linear
setting. Interestingly, they have a few brief discussionson the
difficulties to optimize at the meta-level. (c.f. Meta-level
conditioning section.) Overall, it shines
light on the ground idea behind IDBD.
TIDBD: Adapting Temporal-difference Step-sizes Through
Stochastic Meta-descent
by Kearney, Alex and Veeriah, Vivek and Travnik, Jaden B and
Sutton, Richard S and Pilarski, Patrick M
http://arxiv.org/abs/1804.03334
Increased rates of convergence through learning rate adaptation
by Jacobs, Robert A
http://www.sciencedirect.com/science/article/pii/0893608088900032
This paper argues that we need (at least) four ingredients
to improve optimization of connectionist
networks: 1. each parameter has its own
stepsize, 2. stepsizes vary over time, 3. if
consecutive gradients of a stepsize have the same sign, the
stepsize should be increased, 4. conversely, if the
stepsize should be decreased if its gradients have opposite
signs. It also proposes to use two
improvements: 1. Momentum (i.e. Polyak's
heavyball), 2. delta-bar-delta (i.e. learning the
stepsize). It has an interesting comment
on the difficulty of learning the stepsize, and therefore comes
up with a ``hack'' that outperforms
momentum.
Meta-descent for Online, Continual Prediction
by Jacobsen, Andrew and Schlegel, Matthew and Linke, Cameron
and Degris, Thomas and White, Adam and White, Martha
http://arxiv.org/abs/1907.07751
The idea is to learn the learning rate so as to
minimize the norm of the gradient. They argue that for the
continual learning setting, this forces the algorithm to
stay ``as stable as possible''. No
theorems, small-scale (but interesting) experiments.
Adaptation of learning rate parameters
by Sutton, Rich
http://learn2learn.net
Gradient-Based Meta-Learning with Learned Layerwise
Metric and Subspace
by Lee, Yoonho and Choi, Seungjin
http://arxiv.org/abs/1801.05558
Meta-Learning with Warped Gradient Descent
by Flennerhag, Sebastian and Rusu, Andrei A and Pascanu, Razvan
and Yin, Hujun and Hadsell, Raia
http://arxiv.org/abs/1909.00025
Meta-Learning via Learned Loss
by Chebotar, Yevgen and Molchanov, Artem and Bechtle, Sarah and
Righetti, Ludovic and Meier, Franziska and Sukhatme, Gaurav
http://arxiv.org/abs/1906.05374
They learn the loss as a NN, and that loss's objective
is to maximize the sum of rewards. It is provided a bunch of
things, including inputs, outputs,
goals.
Meta-Curvature
by Park, Eunbyung and Oliva, Junier B
http://arxiv.org/abs/1902.03356
Alpha MAML: Adaptive Model-Agnostic Meta-Learning
by Behl, Harkirat Singh and Baydin, At{\i}l{\i}m G{\"u}ne{\c s}
and Torr, Philip H S
http://arxiv.org/abs/1905.07435
They combine hypergradient and MAML: adapt all learning
rates at all times.
Meta-SGD: Learning to Learn Quickly for Few-Shot
Learning
by Li, Zhenguo and Zhou, Fengwei and Chen, Fei and Li, Hang
http://arxiv.org/abs/1707.09835
ProMP: Proximal Meta-Policy Search
by Rothfuss, Jonas and Lee, Dennis and Clavera, Ignasi and Asfour,
Tamim and Abbeel, Pieter
http://arxiv.org/abs/1810.06784
Model-Agnostic Meta-Learning for Fast Adaptation of Deep
Networks
by Finn, Chelsea and Abbeel, Pieter and Levine, Sergey
http://learn2learn.net
Optimization as a model for few-shot learning
by Ravi, Sachin and Larochelle, Hugo
https://openreview.net/pdf?id=rJY0-Kcll
Fast Context Adaptation via Meta-Learning
by Zintgraf, Luisa M and Shiarlis, Kyriacos and Kurin, Vitaly
and Hofmann, Katja and Whiteson, Shimon
http://arxiv.org/abs/1810.03642
Meta-Learning with Implicit Gradients
by Rajeswaran, Aravind and Finn, Chelsea and Kakade, Sham and
Levine, Sergey
http://arxiv.org/abs/1909.04630
Natural Neural Networks
by Desjardins, Guillaume and Simonyan, Karen and Pascanu, Razvan
and Kavukcuoglu, Koray
http://dl.acm.org/citation.cfm?id=2969442.2969471
A Baseline for Few-Shot Image Classification
by Dhillon, Guneet S and Chaudhari, Pratik and Ravichandran,
Avinash and Soatto, Stefano
http://arxiv.org/abs/1909.02729
A CLOSER LOOK AT FEW-SHOT CLASSIFICATION
by Chen, Wei-Yu and Liu, Yen-Cheng and Kira, Zsolt
https://openreview.net/pdf?id=HkxLXnAcFQ
Suggests that meta-learning papers haven't been tested against
classical baselines. When considering those baselines, they perform
better than many of the recent meta-learning techniques.
Meta-learning with differentiable closed-form solvers
by Bertinetto, Luca and Henriques, Joao F and Torr, Philip and
Vedaldi, Andrea
https://openreview.net/forum?id=HyxnZh0ct7
Uncertainty in Model-Agnostic Meta-Learning using
Variational Inference
by Nguyen, Cuong and Do, Thanh-Toan and Carneiro, Gustavo
http://arxiv.org/abs/1907.11864
Meta-Reinforcement Learning of Structured Exploration
Strategies
by Gupta, Abhishek and Mendonca, Russell and Liu, Yuxuan and
Abbeel, Pieter and Levine, Sergey
http://arxiv.org/abs/1802.07245
Metalearned Neural Memory
by Munkhdalai, Tsendsuren and Sordoni, Alessandro and Wang,
Tong and Trischler, Adam
http://arxiv.org/abs/1907.09720
Accelerated Stochastic Approximation
by Kesten, Harry
https://projecteuclid.org/euclid.aoms/1177706705
Meta-Learning for Black-box Optimization
by Vishnu, T V and Malhotra, Pankaj and Narwariya, Jyoti and
Vig, Lovekesh and Shroff, Gautam
http://arxiv.org/abs/1907.06901
They essentially extend the recurrent meta-learning
framework in a few ways: 1. Use
regret instead of objective improvement as meta-learning
objective. 2. Normalize the objective so as to
make it play nice with LSTMs. 3. Incorporate
domain-constraints, so that the LSTM always outputs feasible
solutions. All are described in
page 3.
Task Agnostic Continual Learning via Meta Learning
by He, Xu and Sygnowski, Jakub and Galashov, Alexandre and
Rusu, Andrei A and Teh, Yee Whye and Pascanu, Razvan
http://arxiv.org/abs/1906.05201
Watch, Try, Learn: Meta-Learning from Demonstrations and
Reward
by Zhou, Allan and Jang, Eric and Kappler, Daniel and Herzog,
Alex and Khansari, Mohi and Wohlhart, Paul and Bai, Yunfei
and Kalakrishnan, Mrinal and Levine, Sergey and Finn,
Chelsea
http://arxiv.org/abs/1906.03352
Meta-Learning Representations for Continual Learning
by Javed, Khurram and White, Martha
http://arxiv.org/abs/1905.12588
TapNet: Neural Network Augmented with Task-Adaptive
Projection for Few-Shot Learning
by Yoon, Sung Whan and Seo, Jun and Moon, Jaekyun
http://arxiv.org/abs/1905.06549
Meta Reinforcement Learning with Task Embedding and Shared
Policy
by Lan, Lin and Li, Zhenguo and Guan, Xiaohong and Wang,
Pinghui
http://arxiv.org/abs/1905.06527
Hierarchically Structured Meta-learning
by Yao, Huaxiu and Wei, Ying and Huang, Junzhou and Li, Zhenhui
http://arxiv.org/abs/1905.05301
Curious Meta-Controller: Adaptive Alternation between
Model-Based and Model-Free Control in Deep Reinforcement
Learning
by Hafez, Muhammad Burhan and Weber, Cornelius and Kerzel,
Matthias and Wermter, Stefan
http://arxiv.org/abs/1905.01718
Learning to Learn in Simulation
by Teng, Ervin and Iannucci, Bob
http://arxiv.org/abs/1902.01569
Meta-Learning with Differentiable Convex Optimization
by Lee, Kwonjoon and Maji, Subhransu and Ravichandran, Avinash
and Soatto, Stefano
http://arxiv.org/abs/1904.03758
Functional Regularisation for Continual Learning
by Titsias, Michalis K and Schwarz, Jonathan and de G.
Matthews, Alexander G and Pascanu, Razvan and Teh, Yee Whye
http://arxiv.org/abs/1901.11356
Learning to Forget for Meta-Learning
by Baik, Sungyong and Hong, Seokil and Lee, Kyoung Mu
http://arxiv.org/abs/1906.05895
Meta-learning of Sequential Strategies
by Ortega, Pedro A and Wang, Jane X and Rowland, Mark and
Genewein, Tim and Kurth-Nelson, Zeb and Pascanu, Razvan and
Heess, Nicolas and Veness, Joel and Pritzel, Alex and
Sprechmann, Pablo and Jayakumar, Siddhant M and McGrath, Tom
and Miller, Kevin and Azar, Mohammad and Osband, Ian and
Rabinowitz, Neil and Gy{\"o}rgy, Andr{\'a}s and Chiappa,
Silvia and Osindero, Simon and Teh, Yee Whye and van
Hasselt, Hado and de Freitas, Nando and Botvinick, Matthew
and Legg, Shane
http://arxiv.org/abs/1905.03030
This paper essentially provides a theoretical framework to
ground the fact that recurrent meta-learning (RL^2, LLGD^2)
performs Bayesian inference during adaptation.
Auto-Meta: Automated Gradient Based Meta Learner Search
by Kim, Jaehong and Lee, Sangyeul and Kim, Sungwan and Cha,
Moonsu and Lee, Jung Kwon and Choi, Youngduck and Choi,
Yongseok and Cho, Dong-Yeon and Kim, Jiwon
http://arxiv.org/abs/1806.06927
Adaptive Gradient-Based Meta-Learning Methods
by Khodak, Mikhail and Florina-Balcan, Maria and Talwalkar,
Ameet
http://arxiv.org/abs/1906.02717
Embedded Meta-Learning: Toward more flexible deep-learning
models
by Lampinen, Andrew K and McClelland, James L
http://arxiv.org/abs/1905.09950
Modular meta-learning
by Alet, Ferran and Lozano-P{\'e}rez, Tom{\'a}s and Kaelbling,
Leslie P
http://arxiv.org/abs/1806.10166
MetaPred: Meta-Learning for Clinical Risk Prediction
with Limited Patient Electronic Health Records
by Zhang, Xi Sheryl and Tang, Fengyi and Dodge, Hiroko and
Zhou, Jiayu and Wang, Fei
http://arxiv.org/abs/1905.03218
Prototypical Networks for Few-shot Learning
by Snell, Jake and Swersky, Kevin and Zemel, Richard S
http://arxiv.org/abs/1703.05175
Meta-learners' learning dynamics are unlike learners'
by Rabinowitz, Neil C
http://arxiv.org/abs/1905.01320
Backpropamine: training self-modifying neural networks with
differentiable neuromodulated plasticity
by Miconi, Thomas and Rawal, Aditya and Clune, Jeff and Stanley,
Kenneth O
https://openreview.net/forum?id=r1lrAiA5Ym
Reinforcement Learning, Fast and Slow
by Botvinick, Matthew and Ritter, Sam and Wang, Jane X and
Kurth-Nelson, Zeb and Blundell, Charles and Hassabis, Demis
http://dx.doi.org/10.1016/j.tics.2019.02.006
Been There, Done That: Meta-Learning with Episodic Recall
by Ritter, Samuel and Wang, Jane X and Kurth-Nelson, Zeb and
Jayakumar, Siddhant M and Blundell, Charles and Pascanu,
Razvan and Botvinick, Matthew
http://arxiv.org/abs/1805.09692
Guided Meta-Policy Search
by Mendonca, Russell and Gupta, Abhishek and Kralev, Rosen and
Abbeel, Pieter and Levine, Sergey and Finn, Chelsea
http://arxiv.org/abs/1904.00956
Hierarchical Meta Learning
by Zou, Yingtian and Feng, Jiashi
http://arxiv.org/abs/1904.09081
A Meta-Transfer Objective for Learning to Disentangle
Causal Mechanisms
by Bengio, Yoshua and Deleu, Tristan and Rahaman, Nasim and Ke,
Rosemary and Lachapelle, S{\'e}bastien and Bilaniuk, Olexa
and Goyal, Anirudh and Pal, Christopher
http://arxiv.org/abs/1901.10912
Generalize Across Tasks: Efficient Algorithms for Linear
Representation Learning
by Bullins, Brian and Hazan, Elad and Kalai, Adam and Livni, Roi
http://proceedings.mlr.press/v98/bullins19a.html
Incremental Learning-to-Learn with Statistical Guarantees
by Denevi, Giulia and Ciliberto, Carlo and Stamos, Dimitris and
Pontil, Massimiliano
http://arxiv.org/abs/1803.08089
A Model of Inductive Bias Learning
by Baxter, J
http://arxiv.org/abs/1106.0245
Efficient Off-Policy Meta-Reinforcement Learning via
Probabilistic Context Variables
by Rakelly, Kate and Zhou, Aurick and Quillen, Deirdre and
Finn, Chelsea and Levine, Sergey
http://arxiv.org/abs/1903.08254
Continual Learning with Tiny Episodic Memories
by Chaudhry, Arslan and Rohrbach, Marcus and Elhoseiny, Mohamed
and Ajanthan, Thalaiyasingam and Dokania, Puneet K and Torr,
Philip H S and Ranzato, Marc'aurelio
http://arxiv.org/abs/1902.10486
Online Meta-Learning
by Finn, Chelsea and Rajeswaran, Aravind and Kakade, Sham and
Levine, Sergey
http://arxiv.org/abs/1902.08438
Modulating transfer between tasks in gradient-based meta-learning
by Grant, Erin and Jerfel, Ghassen and Heller, Katherine and
Griffiths, Thomas L
https://openreview.net/pdf?id=HyxpNnRcFX
Learning to Adapt in Dynamic, Real-World Environments
Through Meta-Reinforcement Learning
by Nagabandi, Anusha and Clavera, Ignasi and Liu, Simin and
Fearing, Ronald S and Abbeel, Pieter and Levine, Sergey and
Finn, Chelsea
http://arxiv.org/abs/1803.11347
Meta-Learning with Latent Embedding Optimization
by Rusu, Andrei A and Rao, Dushyant and Sygnowski, Jakub and
Vinyals, Oriol and Pascanu, Razvan and Osindero, Simon and
Hadsell, Raia
http://arxiv.org/abs/1807.05960
Learning to Generalize: Meta-Learning for Domain
Generalization
by Li, Da and Yang, Yongxin and Song, Yi-Zhe and Hospedales,
Timothy M
http://arxiv.org/abs/1710.03463
Some Considerations on Learning to Explore via
Meta-Reinforcement Learning
by Stadie, Bradly C and Yang, Ge and Houthooft, Rein and Chen,
Xi and Duan, Yan and Wu, Yuhuai and Abbeel, Pieter and
Sutskever, Ilya
http://arxiv.org/abs/1803.01118
How to train your MAML
by Antoniou, Antreas and Edwards, Harrison and Storkey, Amos
http://arxiv.org/abs/1810.09502
Bayesian Model-Agnostic Meta-Learning
by Kim, Taesup and Yoon, Jaesik and Dia, Ousmane and Kim,
Sungwoong and Bengio, Yoshua and Ahn, Sungjin
http://arxiv.org/abs/1806.03836
Probabilistic Model-Agnostic Meta-Learning
by Finn, Chelsea and Xu, Kelvin and Levine, Sergey
http://arxiv.org/abs/1806.02817
The effects of negative adaptation in Model-Agnostic
Meta-Learning
by Deleu, Tristan and Bengio, Yoshua
http://arxiv.org/abs/1812.02159
Memory-based Parameter Adaptation
by Sprechmann, Pablo and Jayakumar, Siddhant M and Rae, Jack W
and Pritzel, Alexander and Badia, Adri{`a} Puigdom{`e}nech
and Uria, Benigno and Vinyals, Oriol and Hassabis, Demis and
Pascanu, Razvan and Blundell, Charles
http://arxiv.org/abs/1802.10542
Deep Meta-Learning: Learning to Learn in the Concept Space
by Zhou, Fengwei and Wu, Bin and Li, Zhenguo
http://arxiv.org/abs/1802.03596
Deep Prior
by Lacoste, Alexandre and Boquet, Thomas and Rostamzadeh, Negar
and Oreshkin, Boris and Chung, Wonchang and Krueger, David
http://arxiv.org/abs/1712.05016
Recasting Gradient-Based Meta-Learning as Hierarchical
Bayes
by Grant, Erin and Finn, Chelsea and Levine, Sergey and
Darrell, Trevor and Griffiths, Thomas
http://arxiv.org/abs/1801.08930
WNGrad: Learn the Learning Rate in Gradient Descent
by Wu, Xiaoxia and Ward, Rachel and Bottou, L{\'e}on
http://arxiv.org/abs/1803.02865
Learning to Learn
by Finn, Chelsea
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/
Continuous Adaptation via Meta-Learning in Nonstationary
and Competitive Environments
by Al-Shedivat, Maruan and Bansal, Trapit and Burda, Yuri and
Sutskever, Ilya and Mordatch, Igor and Abbeel, Pieter
http://arxiv.org/abs/1710.03641