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Many datasets are underspecified: there exist multiple equally viable solutions to a given task.
A treatise on probability
John Maynard Keynes · 1921
Earlier work this paper cites.
Information theory and statistical mechanics
Edwin T Jaynes · 1957
Earlier work this paper cites.
Neural network ensembles
Lars Kai Hansen and Peter Salamon · 1990
Earlier work this paper cites.
Principles of risk minimization for learning theory
Vladimir Vapnik · 1992
Earlier work this paper cites.
Neural network ensembles, cross validation, and active learning
Anders Krogh, Jesper Vedelsby, et al · 1995
Earlier work this paper cites.
Active learning with statistical models
David A Cohn, Zoubin Ghahramani, and Michael I Jordan · 1996
Earlier work this paper cites.
Ensemble methods in machine learning
Thomas G Dietterich · 2000
Earlier work this paper cites.
Causality: models, reasoning and inference , volume 19
Judea Pearl · 2000
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Bayesian active learning for classification and preference learning
Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, and Máté Lengyel · 2011
Earlier work this paper cites.
Novelty search and the problem with objectives
Joel Lehman and Kenneth O Stanley · 2011
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
Earlier work this paper cites.
The mnist database of handwritten digit images for machine learning research
Li Deng · 2012
Earlier work this paper cites.
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee et al · 2013
Earlier work this paper cites.
Theory of disagreement-based active learning
Steve Hanneke et al · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
Earlier work this paper cites.
Deep domain confusion: Maximizing for domain invariance
Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell · 2014
Earlier work this paper cites.
Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Earlier work this paper cites.
Illuminating search spaces by mapping elites
Jean-Baptiste Mouret and Jeff Clune · 2015
Earlier work this paper cites.
Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
Earlier work this paper cites.
Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
Earlier work this paper cites.
Correlation alignment for unsupervised domain adaptation
Baochen Sun, Jiashi Feng, and Kate Saenko · 2016
Earlier work this paper cites.
A closer look at memorization in deep networks
Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al · 2017
Earlier work this paper cites.
Quality and diversity optimization: A unifying modular framework
Antoine Cully and Yiannis Demiris · 2017
Cited alongside, same era.
beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
Cited alongside, same era.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
Cited alongside, same era.
Causal effect inference with deep latent-variable models
Christos Louizos, Uri Shalit, Joris Mooij, David Sontag, Richard Zemel, and Max Welling · 2017
Cited alongside, same era.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
Cited alongside, same era.
Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann · 2020
Later among the works it cites.
One solution is not all you need: Few-shot extrapolation via structured maxent rl
Saurabh Kumar, Aviral Kumar, Sergey Levine, and Chelsea Finn · 2020
Later among the works it cites.
Large-scale methods for distributionally robust optimization
Daniel Levy, Yair Carmon, John C Duchi, and Aaron Sidford · 2020
Later among the works it cites.
Learning from failure: Training debiased classifier from biased classifier
Junhyun Nam, Hyuntak Cha, Sungsoo Ahn, Jaeho Lee, and Jinwoo Shin · 2020
Later among the works it cites.
Hidden stratification causes clinically meaningful failures in machine learning for medical imaging
Luke Oakden-Rayner, Jared Dunnmon, Gustavo Carneiro, and Christopher Ré · 2020
Later among the works it cites.
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