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We propose a two-sample testing procedure based on learned deep neural network representations.
On the problem of the most efficient tests of statistical hypotheses
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Gene dose of apolipoprotein e type 4 allele and the risk of alzheimer’s disease in late onset families
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The alzheimer’s disease neuroimaging initiative
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Support vector machines
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Novel dataset for fine-grained image categorization
Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao, and Li Fei-Fei · 2011
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The Caltech-UCSD Birds-200-2011 Dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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On the efficient calculation of a linear combination of chi-square random variables with an application in counting string vacua
Johannes Bausch · 2013
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A probabilistic theory of pattern recognition , volume 31
Luc Devroye, László Györfi, and Gábor Lugosi · 2013
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Fine-grained visual classification of aircraft
S. Maji, J. Kannala, E. Rahtu, M. Blaschko, and A. Vedaldi · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Fast two-sample testing with analytic representations of probability measures
Kacper P Chwialkowski, Aaditya Ramdas, Dino Sejdinovic, and Arthur Gretton · 2015
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Hypothesis testing in unsupervised domain adaptation with applications in alzheimer’s disease
Hao Zhou, Vamsi K Ithapu, Sathya Narayanan Ravi, Vikas Singh, Grace Wahba, and Sterling C Johnson · 2016
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Very deep convolutional neural networks for raw waveforms
Wei Dai, Chia Dai, Shuhui Qu, Juncheng Li, and Samarjit Das · 2017
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Size-independent sample complexity of neural networks
Noah Golowich, Alexander Rakhlin, and Ohad Shamir · 2017
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Universal function approximation by deep neural nets with bounded width and relu activations
Boris Hanin · 2017
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Mmd gan: Towards deeper understanding of moment matching network
Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, and Barnabás Póczos · 2017
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Avram J Holmes, Marisa O Hollinshead, Timothy M O’Keefe, Victor I Petrov, Gabriele R Fariello, Lawrence L Wald, Bruce Fischl, Bruce R Rosen, Ross W Mair, Joshua L Roffman, et al · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Transfer learning using computational intelligence: a survey
Jie Lu, Vahid Behbood, Peng Hao, Hua Zuo, Shan Xue, and Guangquan Zhang · 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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Interpretable distribution features with maximum testing power
Wittawat Jitkrittum, Zoltán Szabó, Kacper P Chwialkowski, and Arthur Gretton · 2016
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Classification accuracy as a proxy for two sample testing
Ilmun Kim, Aaditya Ramdas, Aarti Singh, and Larry Wasserman · 2016
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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On gradient regularizers for mmd gans
Michael Arbel, Dougal Sutherland, Mikołaj Bińkowski, and Arthur Gretton · 2018
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Mikołaj Bińkowski, Dougal J Sutherland, Michael Arbel, and Arthur Gretton · 2018
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Informative features for model comparison
Wittawat Jitkrittum, Heishiro Kanagawa, Patsorn Sangkloy, James Hays, Bernhard Schölkopf, and Arthur Gretton · 2018
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Foundations of machine learning
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An empirical study on evaluation metrics of generative adversarial networks
Qiantong Xu, Gao Huang, Yang Yuan, Chuan Guo, Yu Sun, Felix Wu, and Kilian Weinberger · 2018
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On the null distribution of bayes factors in linear regression
Quan Zhou and Yongtao Guan · 2018
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Probability: theory and examples , volume 49
Rick Durrett · 2019
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The age of reason
Gramatik · 2019
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