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Kernels are powerful and versatile tools in machine learning and statistics.
Kernel change-point detection with auxiliary deep generative models
Chang, W.-C., Li, C.-L., Yang, Y., and Póczos, B. (2019) · 1901
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Directional derivatives of the maximum function
Borisenko, O. and Minchenko, L. (1992) · 1992
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Bayesian neural networks and density networks
MacKay, D. J. (1995) · 1995
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998) · 1998
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On kernel-target alignment
Cristianini, N., Shawe-Taylor, J., Elisseeff, A., and Kandola, J. S. (2002) · 2002
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Multiple kernel learning, conic duality, and the smo algorithm
Bach, F. R., Lanckriet, G. R., and Jordan, M. I. (2004) · 2004
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Learning the kernel matrix with semidefinite programming
Lanckriet, G. R., Cristianini, N., Bartlett, P., Ghaoui, L. E., and Jordan, M. I. (2004) · 2004
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Random features for large-scale kernel machines
Rahimi, A. and Recht, B. (2007) · 2007
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Liblinear: A library for large linear classification
Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., and Lin, C.-J. (2008) · 2008
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Using deep belief nets to learn covariance kernels for gaussian processes
Hinton, G. E. and Salakhutdinov, R. R. (2008) · 2008
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Exploring large feature spaces with hierarchical multiple kernel learning
Bach, F. R. (2009) · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G. (2009) · 2009
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Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning
Rahimi, A. and Recht, B. (2009) · 2009
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Generalization bounds for learning kernels
Cortes, C., Mohri, M., and Rostamizadeh, A. (2010) · 2010
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Multiple kernel learning algorithms
Gönen, M. and Alpaydın, E. (2011) · 2011
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Fourier analysis on groups
Rudin, W. (2011) · 2011
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Fourier kernel learning
Băzăvan, E. G., Li, F., and Sminchisescu, C. (2012) · 2012
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A kernel two-sample test
Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A. (2012) · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012) · 2012
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Learning kernels using local rademacher complexity
Cortes, C., Kloft, M., and Mohri, M. (2013) · 2013
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Structure discovery in nonparametric regression through compositional kernel search
Duvenaud, D., Lloyd, J. R., Grosse, R., Tenenbaum, J. B., and Ghahramani, Z. (2013) · 2013
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Gaussian process kernels for pattern discovery and extrapolation
Wilson, A. and Adams, R. (2013) · 2013
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Scalable kernel methods via doubly stochastic gradients
Dai, B., Xie, B., He, N., Liang, Y., Raj, A., Balcan, M.-F. F., and Song, L. (2014) · 2014
Cited alongside, same era.
Generative adversarial nets
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. C., and Bengio, Y. (2014) · 2014
Cited alongside, same era.
Convolutional kernel networks
Mairal, J., Koniusz, P., Harchaoui, Z., and Schmid, C. (2014) · 2014
An empirical study on the properties of random bases for kernel methods
Alber, M., Kindermans, P.-J., Schütt, K., Müller, K.-R., and Sha, F. (2017) · 2017
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Towards principled methods for training generative adversarial networks
Arjovsky, M. and Bottou, L. (2017) · 2017
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Wasserstein GAN
Arjovsky, M., Chintala, S., and Bottou, L. (2017) · 2017
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The cramer distance as a solution to biased wasserstein gradients
Bellemare, M. G., Danihelka, I., Dabney, W., Mohamed, S., Lakshminarayanan, B., Hoyer, S., and Munos, R. (2017) · 2017
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Data-driven random fourier features using stein effect
Chang, W.-C., Li, C.-L., Yang, Y., and Poczos, B. (2017) · 2017
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Improved training of wasserstein gans
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Cited alongside, same era.
Training generative neural networks via maximum mean discrepancy optimization
Dziugaite, G. K., Roy, D. M., and Ghahramani, Z. (2015) · 2015
Cited alongside, same era.
Generative moment matching networks
Li, Y., Swersky, K., and Zemel, R. (2015) · 2015
Cited alongside, same era.
A la carte–learning fast kernels
Yang, Z., Wilson, A., Smola, A., and Song, L. (2015) · 2015
Cited alongside, same era.
Learning scalable deep kernels with recurrent structure
Al-Shedivat, M., Wilson, A. G., Saatchi, Y., Hu, Z., and Xing, E. P. (2016) · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
Cited alongside, same era.
Utilize old coordinates: Faster doubly stochastic gradients for kernel methods
Li, C.-L. and Póczos, B. (2016) · 2016
Cited alongside, same era.
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. (2017) · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S. (2017) · 2017
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Mmd gan: Towards deeper understanding of moment matching network
Li, C.-L., Chang, W.-C., Cheng, Y., Yang, Y., and Poczos, B. (2017) · 2017
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Fisher gan
Mroueh, Y. and Sercu, T. (2017) · 2017
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Mcgan: Mean and covariance feature matching gan
Mroueh, Y., Sercu, T., and Goel, V. (2017) · 2017
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Bayesian gan
Saatchi, Y. and Wilson, A. G. (2017) · 2017
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A hitting time analysis of stochastic gradient langevin dynamics
Zhang, Y., Liang, P., and Charikar, M. (2017) · 2017
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On gradient regularizers for mmd gans
Arbel, M., Sutherland, D. J., Bińkowski, M., and Gretton, A. (2018) · 2018
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Demystifying mmd gans
Bińkowski, M., Sutherland, D. J., Arbel, M., and Gretton, A. (2018) · 2018
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Not-so-random features
Bullins, B., Zhang, C., and Zhang, Y. (2018) · 2018
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Real Analysis and Probability
Dudley, R. M. (2018) · 2018
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Semi-supervised deep kernel learning: Regression with unlabeled data by minimizing predictive variance
Jean, N., Xie, S. M., and Ermon, S. (2018) · 2018
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Sobolev gan
Mroueh, Y., Li, C.-L., Sercu, T., Raj, A., and Cheng, Y. (2018) · 2018
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