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Many meta-learning approaches for few-shot learning rely on simple base learners such as nearest-neighbor classifiers.
Evolutionary principles in self-referential learning. on learning now to learn: The meta-meta-meta…-hook
Jurgen Schmidhuber · 1987
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Lifelong Learning Algorithms
Sebastian Thrun · 1998
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Support Vector Machines for Multiclass Pattern Recognition
Jason Weston and Chris Watkins · 1999
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On the algorithmic implementation of multiclass kernel-based vector machines
Koby Crammer and Yoram Singer · 2002
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A perspective view and survey of meta-learning
Ricardo Vilalta and Youssef Drissi · 2002
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Learning gaussian conditional random fields for low-level vision
Marshall F. Tappen, Ce Liu, Edward H. Adelson, and William T. Freeman · 2007
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An empirical evaluation of supervised learning in high dimensions
Rich Caruana, Nikos Karampatziakis, and Ainur Yessenalina · 2008
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Implicit functions and solution mappings
Asen L. Dontchev and R. Tyrrell Rockafellar · 2009
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Ensemble of exemplar-svms for object detection and beyond
Tomasz Malisiewicz, Abhinav Gupta, and Alexei A. Efros · 2011
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Generic methods for optimization-based modeling
Justin Domke · 2012
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The implicit function theorem: history, theory, and applications
Steven G. Krantz and Harold R. Parks · 2012
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Distance-based image classification: Generalizing to new classes at near-zero cost
Thomas Mensink, Jakob Verbeek, Florent Perronnin, and Gabriella Csurka · 2013
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Shrinkage fields for effective image restoration
Uwe Schmidt and Stefan Roth · 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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Gradient-based hyperparameter optimization through reversible learning
Dougal Maclaurin, David Duvenaud, and Ryan Adams · 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 Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Stephen Gould, Basura Fernando, Anoop Cherian, Peter Anderson, Rodrigo Santa Cruz, and Edison Guo · 2016
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Dropblock: A regularization method for convolutional networks
Golnaz Ghiasi, Tsung-Yi Lin, and Quoc V. Le · 2018
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Dynamic few-shot visual learning without forgetting
Spyros Gidaris and Nikos Komodakis · 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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Rapid adaptation with conditionally shifted neurons
Tsendsuren Munkhdalai, Xingdi Yuan, Soroush Mehri, and Adam Trischler · 2018
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Tadam: Task dependent adaptive metric for improved few-shot learning
Boris N. Oreshkin, Pau Rodríguez, and Alexandre Lacoste · 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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Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, and Daan Wierstra · 2016
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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OptNet: Differentiable optimization as a layer in neural networks
Brandon Amos and J. Zico Kolter · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard S. Zemel · 2017
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On the Differentiability of the Solution to Convex Optimization Problems
Shane Barratt · 2018
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Meta-learning for semi-supervised few-shot classification
Mengye Ren, Sachin Ravi, Eleni Triantafillou, Jake Snell, Kevin Swersky, Josh B. Tenenbaum, Hugo Larochelle, and Richard S. Zemel · 2018
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Learning to compare: Relation network for few-shot learning
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip H. S. Torr, and Timothy M. Hospedales · 2018
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Meta-learning with differentiable closed-form solvers
Luca Bertinetto, João F. Henriques, Philip H. S. Torr, and Andrea Vedaldi · 2019
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Transductive propagation network for few-shot learning
Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, and Yi Yang · 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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