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Meta-learning enables algorithms to quickly learn a newly encountered task with just a few labeled examples by transferring previously learned knowledge.
Rademacher and gaussian complexities: Risk bounds and structural results
Peter L Bartlett and Shahar Mendelson · 2002
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Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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URL http://www.dermnet.com/
Dermnet dataset, 2016 · 2016
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Rdkit: Open-source cheminformatics software
Greg Landrum · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
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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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Meta-sgd: Learning to learn quickly for few shot learning
Zhenguo Li, Fengwei Zhou, Fei Chen, and Hang Li · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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URL https://github.com/GRAND-Lab/graph_datasets
Nci dataset, 2018 · 2018
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Meta-learning and universality: Deep representations and gradient descent can approximate any learning algorithm
Chelsea Finn and Sergey Levine · 2018
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Probabilistic model-agnostic meta-learning
Chelsea Finn, Kelvin Xu, and Sergey Levine · 2018
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Few-shot learning with graph neural networks
Victor Garcia and Joan Bruna · 2018
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Recasting gradient-based meta-learning as hierarchical bayes
Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, and Thomas Griffiths · 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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A simple neural attentive meta-learner
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel · 2018
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News category dataset, 06 2018
Rishabh Misra · 2018
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Reptile: a scalable metalearning algorithm
Alex Nichol and John Schulman · 2018
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Prototypical clustering networks for dermatological disease diagnosis
Viraj Prabhu, Anitha Kannan, Murali Ravuri, Manish Chablani, David Sontag, and Xavier Amatriain · 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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Meta-learning with latent embedding optimization
Andrei A Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, and Raia Hadsell · 2018
Cited alongside, same era.
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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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2018
Cited alongside, same era.
Image deformation meta-networks for one-shot learning
Zitian Chen, Yanwei Fu, Yu-Xiong Wang, Lin Ma, Wei Liu, and Martial Hebert · 2019
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Online meta-learning
Chelsea Finn, Aravind Rajeswaran, Sham Kakade, and Sergey Levine · 2019
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Towards understanding generalization in gradient-based meta-learning
A baseline for few-shot image classification
Guneet S Dhillon, Pratik Chaudhari, Avinash Ravichandran, and Stefano Soatto · 2020
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Few-shot learning via learning the representation, provably
Simon S Du, Wei Hu, Sham M Kakade, Jason D Lee, and Qi Lei · 2020
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Meta-learning with warped gradient descent
Sebastian Flennerhag, Andrei A Rusu, Razvan Pascanu, Hujun Yin, and Raia Hadsell · 2020
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Meta dropout: Learning to perturb latent features for generalization
Hae Beom Lee, Taewook Nam, Eunho Yang, and Sung Ju Hwang · 2020
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Difficulty-aware meta-learning for rare disease diagnosis
Xiaomeng Li, Lequan Yu, Yueming Jin, Chi-Wing Fu, Lei Xing, and Pheng-Ann Heng · 2020
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Rapid learning or feature reuse? towards understanding the effectiveness of maml
Aniruddh Raghu, Maithra Raghu, Samy Bengio, and Oriol Vinyals · 2020
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Simon Guiroy, Vikas Verma, and Christopher Pal · 2019
Cited alongside, same era.
Task agnostic meta-learning for few-shot learning
Muhammad Abdullah Jamal and Guo-Jun Qi · 2019
Cited alongside, same era.
Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut · 2019
Cited alongside, same era.
Transductive propagation network for few-shot learning
Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, and Yi Yang · 2019
Cited alongside, same era.
Md Ashraful Alam Milton · 2019
Cited alongside, same era.
Meta-curvature
Eunbyung Park and Junier B Oliva · 2019
Cited alongside, same era.
Meta-learning with implicit gradients
Aravind Rajeswaran, Chelsea Finn, Sham M Kakade, and Sergey Levine · 2019
Cited alongside, same era.
Later among the works it cites.
Meta-learning requires meta-augmentation
Janarthanan Rajendran, Alex Irpan, and Eric Jang · 2020
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Provable meta-learning of linear representations
Nilesh Tripuraneni, Chi Jin, and Michael I Jordan · 2020
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Regularizing meta-learning via gradient dropout
Hung-Yu Tseng, Yi-Wen Chen, Yi-Hsuan Tsai, Sifei Liu, Yen-Yu Lin, and Ming-Hsuan Yang · 2020
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Meta-learning without memorization
Mingzhang Yin, George Tucker, Mingyuan Zhou, Sergey Levine, and Chelsea Finn · 2020
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Concept learners for few-shot learning
Kaidi Cao, Maria Brbic, and Jure Leskovec · 2021
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Therapeutics data commons: Machine learning datasets and tasks for therapeutics
Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W Coley, Cao Xiao, Jimeng Sun, and Marinka Zitnik · 2021
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Dreca: A general task augmentation strategy for few-shot natural language inference
Shikhar Murty, Tatsunori B Hashimoto, and Christopher D Manning · 2021
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Data augmentation for meta-learning
Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong, and Tom Goldstein · 2021
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{BOIL}: Towards representation change for few-shot learning
Jaehoon Oh, Hyungjun Yoo, ChangHwan Kim, and Se-Young Yun · 2021
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Bridging multi-task learning and meta-learning: Towards efficient training and effective adaptation
Haoxiang Wang, Han Zhao, and Bo Li · 2021
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Improving generalization in meta-learning via task augmentation
Huaxiu Yao, Longkai Huang, Linjun Zhang, Ying Wei, Li Tian, James Zou, Junzhou Huang, and Zhenhui Li · 2021
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How does mixup help with robustness and generalization?
Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, and James Zou · 2021
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