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Supervised learning typically focuses on learning transferable representations from training examples annotated by humans.
On the uniform convergence of relative frequencies of events to their probabilities
V. N. Vapnik and A. Ya. Chervonenkis · 1971
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Multidimensional scaling, tree-fitting, and clustering
Roger N Shepard · 1980
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Training stochastic model recognition algorithms as networks can lead to maximum mutual information estimation of parameters
John Bridle · 1989
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The information bottleneck method
Naftali Tishby, Fernando C Pereira, and William Bialek · 2000
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Soft learning vector quantization
Sambu Seo and Klaus Obermayer · 2003
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One-shot learning of object categories
Li Fei-Fei, R. Fergus, and P. Perona · 2006
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Generalized non-metric multidimensional scaling
Sameer Agarwal, Josh Wills, Lawrence Cayton, Gert Lanckriet, David Kriegman, and Serge Belongie · 2007
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80 million tiny images: A large data set for nonparametric object and scene recognition
Antonio Torralba, Rob Fergus, and William T Freeman · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Automatic category label coarsening for syntax-based machine translation
Greg Hanneman and Alon Lavie · 2011
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Low-dimensional embedding using adaptively selected ordinal data
Kevin G Jamieson and Robert D Nowak · 2011
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Lost without a compass: Nonmetric triangulation and landmark multidimensional scaling
Mark A. Davenport · 2013
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Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 2013
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Deep metric learning using triplet network
Elad Hoffer and Nir Ailon · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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What makes ImageNet good for transfer learning?
Minyoung Huh, Pulkit Agrawal, and Alexei A Efros · 2016
Cited alongside, same era.
From softmax to sparsemax: A sparse model of attention and multi-label classification
Andre Martins and Ramon Astudillo · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Xavier Gastaldi · 2017
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In defense of the triplet loss for person re-identification
Alexander Hermans, Lucas Beyer, and Bastian Leibe · 2017
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Gibbs sampling with people
Peter Harrison, Raja Marjieh, Federico Adolfi, Pol van Rijn, Manuel Anglada-Tort, Ofer Tchernichovski, Pauline Larrouy-Maestri, and Nori Jacoby · 2020
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Revealing the multidimensional mental representations of natural objects underlying human similarity judgements
Martin N Hebart, Charles Y Zheng, Francisco Pereira, and Chris I Baker · 2020
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Supervised contrastive learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan · 2020
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End-to-end deep prototype and exemplar models for predicting human behavior
Pulkit Singh, Joshua C Peterson, Ruairidh M Battleday, and Thomas L Griffiths · 2020
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2017
Cited alongside, same era.
Analysis and optimization of loss functions for multiclass, top-k, and multilabel classification
Maksim Lapin, Matthias Hein, and Bernt Schiele · 2017
Cited alongside, same era.
CINIC-10 is not imagenet or CIFAR-10
Luke Nicholas Darlow, Elliot J. Crowley, Antreas Antoniou, and Amos J. Storkey · 2018
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Cited alongside, same era.
Evaluating (and improving) the correspondence between deep neural networks and human representations
Joshua C Peterson, Joshua T Abbott, and Thomas L Griffiths · 2018
Cited alongside, same era.
Do cifar-10 classifiers generalize to cifar-10?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2018
Cited alongside, same era.
Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V. Le · 2020
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Iterative teaching by label synthesis
Weiyang Liu, Zhen Liu, Hanchen Wang, Liam Paull, Bernhard Schölkopf, and Adrian Weller · 2021
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Imagenet-21k pretraining for the masses
Tal Ridnik, Emanuel Ben-Baruch, Asaf Noy, and Lihi Zelnik-Manor · 2021
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Enriching ImageNet with human similarity judgments and psychological embeddings
Brett D Roads and Bradley C Love · 2021
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One line to rule them all: Generating LO-shot soft-label prototypes
Ilia Sucholutskv, Nam-Hwui Kim, Ryan P Browne, and Matthias Schonlau · 2021
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Soft-label dataset distillation and text dataset distillation
Ilia Sucholutsky and Matthias Schonlau · 2021
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Eliciting and learning with soft labels from every annotator
Katherine M Collins, Umang Bhatt, and Adrian Weller · 2022
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Can humans do less-than-one-shot learning?, Jun 2022
Maya Malaviya, Ilia Sucholutsky, Kerem Oktar, and Thomas L Griffiths · 2022
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Words are all you need? capturing human sensory similarity with textual descriptors
Raja Marjieh, Pol van Rijn, Ilia Sucholutsky, Theodore R Sumers, Harin Lee, Thomas L Griffiths, and Nori Jacoby · 2022
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Extracting low-dimensional psychological representations from convolutional neural networks
Aditi Jha, Joshua C Peterson, and Thomas L Griffiths · 2023
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