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Recent years have witnessed an abundance of new publications and approaches on meta-learning.
Task2vec: Task embedding for meta-learning
Alessandro Achille, Michael Lam, Rahul Tewari, Avinash Ravichandran, Subhransu Maji, Charless Fowlkes, Stefano Soatto, and Pietro Perona · 1902
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The information complexity of learning tasks, their structure and their distance
Alessandro Achille, Giovanni Paolini, Glen Mbeng, and Stefano Soatto · 1904
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Bagging predictors
Leo Breiman · 1996
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Learning to learn
Sebastian Thrun and Lorien Pratt · 1998
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A model of inductive bias learning
Jonathan Baxter · 2000
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Ensemble methods in machine learning
Thomas G Dietterich · 2000
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A perspective view and survey of meta-learning
Ricardo Vilalta and Youssef Drissi · 2002
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Algorithmic stability and meta-learning
Andreas Maurer · 2005
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Svm-knn: Discriminative nearest neighbor classification for visual category recognition
Hao Zhang, Alexander C Berg, Michael Maire, and Jitendra Malik · 2006
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Multi-task feature learning
Andreas Argyriou, Theodoros Evgeniou, and Massimiliano Pontil · 2007
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Greedy layer-wise training of deep networks
Yoshua Bengio, Pascal Lamblin, Dan Popovici, and Hugo Larochelle · 2007
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Shared segmentation of natural scenes using dependent pitman-yor processes
Erik B. Sudderth and Michael I. Jordan · 2008
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Recognizing indoor scenes
Ariadna Quattoni and Antonio Torralba · 2009
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Distance metric learning for large margin nearest neighbor classification
Kilian Q Weinberger and Lawrence K Saul · 2009
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A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2010
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Why does unsupervised pre-training help deep learning?
Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, and Samy Bengio · 2010
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A survey on transfer learning
Sinno Jialin Pan, Qiang Yang, et al · 2010
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Novel dataset for fine-grained image categorization
Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao, and Li Fei-Fei · 2011
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Learning to share visual appearance for multiclass object detection
Ruslan Salakhutdinov, Antonio Torralba, and Josh Tenenbaum · 2011
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The Caltech-UCSD Birds-200-2011 Dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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Geodesic flow kernel for unsupervised domain adaptation
Boqing Gong, Yuan Shi, Fei Sha, and Kristen Grauman · 2012
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Ensemble methods: foundations and algorithms
Zhi-Hua Zhou · 2012
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Facial age estimation based on label-sensitive learning and age-oriented regression
Wei-Lun Chao, Jun-Zuo Liu, and Jian-Jiun Ding · 2013
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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Fine-grained visual classification of aircraft
S. Maji, J. Kannala, E. Rahtu, M. Blaschko, and A. Vedaldi · 2013
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2014
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Understanding machine learning: From theory to algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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Capturing long-tail distributions of object subcategories
Xiangxin Zhu, Dragomir Anguelov, and Deva Ramanan · 2014
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Metalearning: a survey of trends and technologies
Christiane Lemke, Marcin Budka, and Bogdan Gabrys · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael S. Bernstein, Alexander C. Berg, and Fei-Fei Li · 2015
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Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando De Freitas · 2016
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Learning feed-forward one-shot learners
Luca Bertinetto, João F Henriques, Jack Valmadre, Philip Torr, and Andrea Vedaldi · 2016
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Rl 2 : Fast reinforcement learning via slow reinforcement learning
Yan Duan, John Schulman, Xi Chen, Peter L Bartlett, Ilya Sutskever, and Pieter Abbeel · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
The benefit of multitask representation learning
Andreas Maurer, Massimiliano Pontil, and Bernardino Romera-Paredes · 2016
Cited alongside, same era.
Meta-learning with memory-augmented neural networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap · 2016
Cited alongside, same era.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
Cited alongside, same era.
Meta learning shared hierarchies
Kevin Frans, Jonathan Ho, Xi Chen, Pieter Abbeel, and John Schulman · 2018
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Supervising unsupervised learning
Vikas Garg and Adam Kalai · 2018
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Dynamic few-shot visual learning without forgetting
Spyros Gidaris and Nikos Komodakis · 2018
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Few-shot human motion prediction via meta-learning
Liang-Yan Gui, Yu-Xiong Wang, Deva Ramanan, and José MF Moura · 2018
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Unsupervised learning via meta-learning
Kyle Hsu, Sergey Levine, and Chelsea Finn · 2018
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Natural language to structured query generation via meta-learning
Po-Sen Huang, Chenglong Wang, Rishabh Singh, Wen-tau Yih, and Xiaodong He · 2018
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Jane X Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, and Matt Botvinick · 2016
Cited alongside, same era.
Learning to learn: Model regression networks for easy small sample learning
Yu-Xiong Wang and Martial Hebert · 2016
Cited alongside, same era.
Learning algorithms for active learning
Philip Bachman, Alessandro Sordoni, and Adam Trischler · 2017
Cited alongside, same era.
Neural optimizer search with reinforcement learning
Irwan Bello, Barret Zoph, Vijay Vasudevan, and Quoc V Le · 2017
Cited alongside, same era.
One-shot imitation learning
Yan Duan, Marcin Andrychowicz, Bradly Stadie, OpenAI Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, and Wojciech Zaremba · 2017
Cited alongside, same era.
Towards a neural statistician
Harrison Edwards and Amos Storkey · 2017
Cited alongside, same era.
A bridge between hyperparameter optimization and larning-to-learn
Luca Franceschi, Michele Donini, Paolo Frasconi, and Massimiliano Pontil · 2017
Cited alongside, same era.
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Few-shot learning with meta-learning: Progress made and challenges ahead
Hugo Larochelle · 2018
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Gradient-based meta-learning with learned layerwise metric and subspace
Yoonho Lee and Seungjin Choi · 2018
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Learning unsupervised learning rules
Luke Metz, Niru Maheswaranathan, Brian Cheung, and Jascha Sohl-Dickstein · 2018
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A simple neural attentive meta-learner
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel · 2018
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On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman · 2018
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Tadam: Task dependent adaptive metric for improved few-shot learning
Boris N Oreshkin, Alexandre Lacoste, and Pau Rodriguez · 2018
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Meta-learning transferable active learning policies by deep reinforcement learning
Kunkun Pang, Mingzhi Dong, Yang Wu, and Timothy Hospedales · 2018
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Few-shot image recognition by predicting parameters from activations
Siyuan Qiao, Chenxi Liu, Wei Shen, and Alan L Yuille · 2018
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Meta-learning for batch mode active learning
Sachin Ravi and Hugo Larochelle · 2018
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Meta-learning for semi-supervised few-shot classification
Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B Tenenbaum, Hugo Larochelle, and Richard S Zemel · 2018
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Been there, done that: Meta-learning with episodic recall
Samuel Ritter, Jane X Wang, Zeb Kurth-Nelson, Siddhant M Jayakumar, Charles Blundell, Razvan Pascanu, and Matthew Botvinick · 2018
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Learning to multi-task by active sampling
Sahil Sharma, Ashutosh Jha, Parikshit Hegde, and Balaraman Ravindran · 2018
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The importance of sampling inmeta-reinforcement learning
Bradly Stadie, Ge Yang, Rein Houthooft, Peter Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, and Ilya Sutskever · 2018
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Learning to compare: Relation network for few-shot learning
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales · 2018
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Joaquin Vanschoren · 2018
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Learning to Learn for Small Sample Visual Recognition
Yu-Xiong Wang · 2018
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Low-shot learning from imaginary data
Yu-Xiong Wang, Ross Girshick, Martial Hebert, and Bharath Hariharan · 2018
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Learning embedding adaptation for few-shot learning
Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, and Fei Sha · 2018
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Transfer learning via learning to transfer
Wei Ying, Yu Zhang, Junzhou Huang, and Qiang Yang · 2018
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One-shot imitation from observing humans via domain-adaptive meta-learning
Tianhe Yu, Chelsea Finn, Annie Xie, Sudeep Dasari, Tianhao Zhang, Pieter Abbeel, and Sergey Levine · 2018
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A closer look at few-shot classification
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang · 2019
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Learning to adapt in dynamic, real-world environments through meta-reinforcement learning
Ignasi Clavera, Anusha Nagabandi, Simin Liu, Ronald S Fearing, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2019
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Feature-critic networks for heterogeneous domain generalization
Yiying Li, Yongxin Yang, Wei Zhou, and Timothy M Hospedales · 2019
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Learning to learn without forgetting by maximizing transfer and minimizing interference
Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, and Gerald Tesauro · 2019
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Meta-learning with latent embedding optimization
Andrei A Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, and Raia Hadsell · 2019
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Meta-dataset: A dataset of datasets for learning to learn from few examples
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, and Hugo Larochelle · 2019
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