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Extraneous variables are variables that are irrelevant for a certain task, but heavily affect the distribution of the available data.
On the elimination of nuisance parameters
Basu, D · 1977
Earlier work this paper cites.
On a formula for the distribution of the maximum likelihood estimator
Barndorff-Nielsen, O · 1983
Earlier work this paper cites.
Parameter orthogonality and approximate conditional inference
Cox, D. R. and Reid, N · 1987
Earlier work this paper cites.
A simple method for the adjustment of profile likelihoods
McCullagh, P. and Tibshirani, R · 1990
Earlier work this paper cites.
Applied linear statistical models , volume 4
Neter, J., Kutner, M. H., Nachtsheim, C. J., and Wasserman, W · 1996
Earlier work this paper cites.
Integrated likelihood methods for eliminating nuisance parameters
Berger, J. O., Liseo, B., and Wolpert, R. L · 1999
Earlier work this paper cites.
Singular value decomposition for genome-wide expression data processing and modeling
Alter, O., Brown, P. O., and Botstein, D · 2000
Earlier work this paper cites.
Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection
Li, C. and Wong, W. H · 2001
Earlier work this paper cites.
An introduction to variable and feature selection
Guyon, I. and Elisseeff, A · 2003
Earlier work this paper cites.
Likelihood inference in the presence of nuisance parameters
Reid, N · 2003
Earlier work this paper cites.
Statistics for experimenters: design, innovation, and discovery , volume 2
Box, G. E., Hunter, J. S., and Hunter, W. G · 2005
Earlier work this paper cites.
An attempt for combining microarray data sets by adjusting gene expressions
Kim, K.-Y., Kim, S. H., Ki, D. H., Jeong, J., Jeong, H. J., Jeung, H.-C., Chung, H. C., and Rha, S. Y · 2007
Earlier work this paper cites.
Capturing heterogeneity in gene expression studies by surrogate variable analysis
Leek, J. T. and Storey, J. D · 2007
Earlier work this paper cites.
The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets–improving meta-analysis and prediction of prognosis
Sims, A. H., Smethurst, G. J., Hey, Y., Okoniewski, M. J., Pepper, S. D., Howell, A., Miller, C. J., and Clarke, R. B · 2008
Cited alongside, same era.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Cited alongside, same era.
Normalization as a canonical neural computation
Carandini, M. and Heeger, D. J · 2012
Cited alongside, same era.
Batch effect removal methods for microarray gene expression data integration: a survey
Lazar, C., Meganck, S., Taminau, J., Steenhoff, D., Coletta, A., Molter, C., Weiss-Solís, D. Y., Duque, R., Bersini, H., and Nowé, A · 2012
Cited alongside, same era.
Transfer feature learning with joint distribution adaptation
Long, M., Wang, J., Ding, G., Sun, J., and Yu, P. S · 2013
Cited alongside, same era.
Unsupervised domain adaptation with residual transfer networks
Long, M., Zhu, H., Wang, J., and Jordan, M. I · 2016
Later among the works it cites.
Instance normalization: The missing ingredient for fast stylization
Ulyanov, D., Vedaldi, A., and Lempitsky, V · 2016
Later among the works it cites.
Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
Later among the works it cites.
Arbitrary style transfer in real-time with adaptive instance normalization
Huang, X. and Belongie, S · 2017
Later among the works it cites.
Deep transfer learning with joint adaptation networks
Long, M., Zhu, H., Wang, J., and Jordan, M. I · 2017
Later among the works it cites.
Adversarial discriminative domain adaptation
Tzeng, E., Hoffman, J., Saenko, K., and Darrell, T · 2017
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Gao, Y., Vedula, S. S., Reiley, C. E., Ahmidi, N., Varadarajan, B., Lin, H. C., Tao, L., Zappella, L., Béjar, B., Yuh, D. D., et al · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Density modeling of images using a generalized normalization transformation
Ballé, J., Laparra, V., and Simoncelli, E. P · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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End-to-end optimized image compression
Ballé, J., Laparra, V., and Simoncelli, E. P · 2016
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Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., and Lempitsky, V · 2016
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Two at once: Enhancing learning and generalization capacities via ibn-net
Pan, X., Luo, P., Shi, J., and Tang, X · 2018
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How does batch normalization help optimization?
Santurkar, S., Tsipras, D., Ilyas, A., and Madry, A · 2018
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Explanation of variability and removal of confounding factors from data through optimal transport
Tabak, E. G. and Trigila, G · 2018
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Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 2019
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Singh, S. and Krishnan, S · 2019
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