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Edwin Lughofer | Thomas Natschläger | Susanne Saminger-Platz | Thomas Grubinger | Werner Zellinger | T. Natschläger | E. Lughofer | W. Zellinger | Thomas Grubinger | Susanne Saminger-Platz
[1] Huaiyu Zhu. On Information and Sufficiency , 1997 .
[2] Bernhard Schölkopf,et al. A Kernel Method for the Two-Sample-Problem , 2006, NIPS.
[3] Chih-Jen Lin,et al. A Practical Guide to Support Vector Classication , 2008 .
[4] Bernhard Schölkopf,et al. Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions , 2009, NIPS.
[5] Koby Crammer,et al. A theory of learning from different domains , 2010, Machine Learning.
[6] Yoram Singer,et al. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..
[7] Trevor Darrell,et al. Adapting Visual Category Models to New Domains , 2010, ECCV.
[8] Qiang Yang,et al. Cross Validation Framework to Choose amongst Models and Datasets for Transfer Learning , 2010, ECML/PKDD.
[9] Ivor W. Tsang,et al. Domain Adaptation via Transfer Component Analysis , 2009, IEEE Transactions on Neural Networks.
[10] Yoshua Bengio,et al. Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach , 2011, ICML.
[11] R. Bass. Convergence of probability measures , 2011 .
[12] Bernhard Schölkopf,et al. A Kernel Two-Sample Test , 2012, J. Mach. Learn. Res..
[13] Luis Fuentes García,et al. The smallest upper bound for the pth absolute central moment of a class of random variables , 2012 .
[14] Matthew D. Zeiler. ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.
[15] Kilian Q. Weinberger,et al. Marginalized Denoising Autoencoders for Domain Adaptation , 2012, ICML.
[16] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[17] J. Norris. Appendix: probability and measure , 1997 .
[18] Sumit Chopra,et al. DLID: Deep Learning for Domain Adaptation by Interpolating between Domains , 2013 .
[19] Brian C. Lovell,et al. Unsupervised Domain Adaptation by Domain Invariant Projection , 2013, 2013 IEEE International Conference on Computer Vision.
[20] Trevor Darrell,et al. Deep Domain Confusion: Maximizing for Domain Invariance , 2014, CVPR 2014.
[21] Rémi Emonet,et al. Landmarks-based kernelized subspace alignment for unsupervised domain adaptation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[23] Fuzhen Zhuang,et al. Supervised Representation Learning: Transfer Learning with Deep Autoencoders , 2015, IJCAI.
[24] Deyu Meng,et al. FastMMD: Ensemble of Circular Discrepancy for Efficient Two-Sample Test , 2014, Neural Computation.
[25] R. Zemel,et al. THE VARIATIONAL FAIR AUTO ENCODER , 2015 .
[26] Michael I. Jordan,et al. Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.
[27] Richard S. Zemel,et al. Generative Moment Matching Networks , 2015, ICML.
[28] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[29] Kate Saenko,et al. Deep CORAL: Correlation Alignment for Deep Domain Adaptation , 2016, ECCV Workshops.
[30] François Laviolette,et al. Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..
[31] Max Welling,et al. The Variational Fair Autoencoder , 2015, ICLR.
[32] Kate Saenko,et al. Return of Frustratingly Easy Domain Adaptation , 2015, AAAI.
[33] Jiaying Liu,et al. Revisiting Batch Normalization For Practical Domain Adaptation , 2016, ICLR.
[34] Alexander J. Smola,et al. Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy , 2016, ICLR.
[35] Omer Levy,et al. Published as a conference paper at ICLR 2018 S IMULATING A CTION D YNAMICS WITH N EURAL P ROCESS N ETWORKS , 2018 .