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Developing algorithms that are able to generalize to a novel task given only a few labeled examples represents a fundamental challenge in closing the gap between machine- and human-level performance.
Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook
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SCOP: a structural classification of proteins database for the investigation of sequences and structures
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Gene Ontology: tool for the unification of biology
Michael Ashburner, Catherine A Ball, Judith A Blake, David Botstein, Heather Butler, J Michael Cherry, Allan P Davis, Kara Dolinski, Selina S Dwight, Janan T Eppig, et al · 2000
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Learning from one example through shared densities on transforms
Erik G Miller, Nicholas E Matsakis, and Paul A Viola · 2000
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Latent Dirichlet Allocation
David M Blei, Andrew Y Ng, and Michael I Jordan · 2003
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Rcv1: A new benchmark collection for text categorization research
David D Lewis, Yiming Yang, Tony G Rose, and Fan Li · 2004
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One-shot learning of object categories
Li Fei-Fei, Rob Fergus, and Pietro Perona · 2006
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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One shot learning of simple visual concepts
Brenden Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua Tenenbaum · 2011
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The Caltech-UCSD Birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Siamese neural networks for one-shot image recognition
Gregory Koch, Richard Zemel, and Ruslan Salakhutdinov · 2015
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Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 2015
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One shot learning via compositions of meaningful patches
Alex Wong and Alan L Yuille · 2015
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“Why should I trust you?” Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Grad-CAM: Why did you say that?
Ramprasaath R Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, and Dhruv Batra · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, and Daan Wierstra · 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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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2017
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SmoothGrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
Unsupervised discovery of object landmarks as structural representations
Yuting Zhang, Yijie Guo, Yixin Jin, Yijun Luo, Zhiyuan He, and Honglak Lee · 2018
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Infinite mixture prototypes for few-shot learning
Kelsey Allen, Evan Shelhamer, Hanul Shin, and Joshua Tenenbaum · 2019
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How to train your MAML
Antreas Antoniou, Harrison Edwards, and Amos Storkey · 2019
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The Gene Ontology resource: 20 years and still GOing strong
Gene Ontology Consortium · 2019
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Diversity with cooperation: Ensemble methods for few-shot classification
Nikita Dvornik, Cordelia Schmid, and Julien Mairal · 2019
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Towards automatic concept-based explanations
Amirata Ghorbani, James Wexler, James Y Zou, and Been Kim · 2019
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris
Tabula Muris Consortium · 2018
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Dynamic few-shot visual learning without forgetting
Spyros Gidaris and Nikos Komodakis · 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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Unsupervised learning of object landmarks through conditional image generation
Tomas Jakab, Ankush Gupta, Hakan Bilen, and Andrea Vedaldi · 2018
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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV)
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Cross attention network for few-shot classification
Ruibing Hou, Hong Chang, MA Bingpeng, Shiguang Shan, and Xilin Chen · 2019
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Meta-learning with differentiable convex optimization
Kwonjoon Lee, Subhransu Maji, Avinash Ravichandran, and Stefano Soatto · 2019
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Learning to propagate for graph meta-learning
Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, and Chengqi Zhang · 2019
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PartNet: A large-scale benchmark for fine-grained and hierarchical part-level 3D object understanding
Kaichun Mo, Shilin Zhu, Angel X Chang, Li Yi, Subarna Tripathi, Leonidas J Guibas, and Hao Su · 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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Learning compositional representations for few-shot recognition
Pavel Tokmakov, Yu-Xiong Wang, and Martial Hebert · 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, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, et al · 2019
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Adaptive cross-modal few-shot learning
Chen Xing, Negar Rostamzadeh, Boris Oreshkin, and Pedro O Pinheiro · 2019
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Mars: discovering novel cell types across heterogeneous single-cell experiments
Maria Brbić, Marinka Zitnik, Sheng Wang, Angela O Pisco, Russ B Altman, Spyros Darmanis, and Jure Leskovec · 2020
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A single cell transcriptomic atlas characterizes aging tissues in the mouse
Tabula Muris Consortium · 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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Deepemd: Few-shot image classification with differentiable earth mover’s distance and structured classifiers
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