Добірка наукової літератури з теми "Maximum Mean Discrepancy (MMD)"
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Статті в журналах з теми "Maximum Mean Discrepancy (MMD)"
Huang, Qihang, Yulin He, and Zhexue Huang. "A Novel Maximum Mean Discrepancy-Based Semi-Supervised Learning Algorithm." Mathematics 10, no. 1 (December 23, 2021): 39. http://dx.doi.org/10.3390/math10010039.
Повний текст джерелаZhou, Zhaokun, Yuanhong Zhong, Xiaoming Liu, Qiang Li, and Shu Han. "DC-MMD-GAN: A New Maximum Mean Discrepancy Generative Adversarial Network Using Divide and Conquer." Applied Sciences 10, no. 18 (September 14, 2020): 6405. http://dx.doi.org/10.3390/app10186405.
Повний текст джерелаXu, Haoji. "Generate Faces Using Ladder Variational Autoencoder with Maximum Mean Discrepancy (MMD)." Intelligent Information Management 10, no. 04 (2018): 108–13. http://dx.doi.org/10.4236/iim.2018.104009.
Повний текст джерелаSun, Jiancheng. "Complex Network Construction of Univariate Chaotic Time Series Based on Maximum Mean Discrepancy." Entropy 22, no. 2 (January 24, 2020): 142. http://dx.doi.org/10.3390/e22020142.
Повний текст джерелаZhang, Xiangqing, Yan Feng, Shun Zhang, Nan Wang, Shaohui Mei, and Mingyi He. "Semi-Supervised Person Detection in Aerial Images with Instance Segmentation and Maximum Mean Discrepancy Distance." Remote Sensing 15, no. 11 (June 4, 2023): 2928. http://dx.doi.org/10.3390/rs15112928.
Повний текст джерелаZhao, Ji, and Deyu Meng. "FastMMD: Ensemble of Circular Discrepancy for Efficient Two-Sample Test." Neural Computation 27, no. 6 (June 2015): 1345–72. http://dx.doi.org/10.1162/neco_a_00732.
Повний текст джерелаWilliamson, Sinead A., and Jette Henderson. "Understanding Collections of Related Datasets Using Dependent MMD Coresets." Information 12, no. 10 (September 23, 2021): 392. http://dx.doi.org/10.3390/info12100392.
Повний текст джерелаLi, Kangji, Borui Wei, Qianqian Tang, and Yufei Liu. "A Data-Efficient Building Electricity Load Forecasting Method Based on Maximum Mean Discrepancy and Improved TrAdaBoost Algorithm." Energies 15, no. 23 (November 22, 2022): 8780. http://dx.doi.org/10.3390/en15238780.
Повний текст джерелаLee, Junghyun, Gwangsu Kim, Mahbod Olfat, Mark Hasegawa-Johnson, and Chang D. Yoo. "Fast and Efficient MMD-Based Fair PCA via Optimization over Stiefel Manifold." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (June 28, 2022): 7363–71. http://dx.doi.org/10.1609/aaai.v36i7.20699.
Повний текст джерелаHan, Chao, Deyun Zhou, Zhen Yang, Yu Xie, and Kai Zhang. "Discriminative Sparse Filtering for Multi-Source Image Classification." Sensors 20, no. 20 (October 16, 2020): 5868. http://dx.doi.org/10.3390/s20205868.
Повний текст джерелаДисертації з теми "Maximum Mean Discrepancy (MMD)"
Cherief-Abdellatif, Badr-Eddine. "Contributions to the theoretical study of variational inference and robustness." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAG001.
Повний текст джерелаThis PhD thesis deals with variational inference and robustness. More precisely, it focuses on the statistical properties of variational approximations and the design of efficient algorithms for computing them in an online fashion, and investigates Maximum Mean Discrepancy based estimators as learning rules that are robust to model misspecification.In recent years, variational inference has been extensively studied from the computational viewpoint, but only little attention has been put in the literature towards theoretical properties of variational approximations until very recently. In this thesis, we investigate the consistency of variational approximations in various statistical models and the conditions that ensure the consistency of variational approximations. In particular, we tackle the special case of mixture models and deep neural networks. We also justify in theory the use of the ELBO maximization strategy, a model selection criterion that is widely used in the Variational Bayes community and is known to work well in practice.Moreover, Bayesian inference provides an attractive online-learning framework to analyze sequential data, and offers generalization guarantees which hold even under model mismatch and with adversaries. Unfortunately, exact Bayesian inference is rarely feasible in practice and approximation methods are usually employed, but do such methods preserve the generalization properties of Bayesian inference? In this thesis, we show that this is indeed the case for some variational inference algorithms. We propose new online, tempered variational algorithms and derive their generalization bounds. Our theoretical result relies on the convexity of the variational objective, but we argue that our result should hold more generally and present empirical evidence in support of this. Our work presents theoretical justifications in favor of online algorithms that rely on approximate Bayesian methods. Another point that is addressed in this thesis is the design of a universal estimation procedure. This question is of major interest, in particular because it leads to robust estimators, a very hot topic in statistics and machine learning. We tackle the problem of universal estimation using a minimum distance estimator based on the Maximum Mean Discrepancy. We show that the estimator is robust to both dependence and to the presence of outliers in the dataset. We also highlight the connections that may exist with minimum distance estimators using L2-distance. Finally, we provide a theoretical study of the stochastic gradient descent algorithm used to compute the estimator, and we support our findings with numerical simulations. We also propose a Bayesian version of our estimator, that we study from both a theoretical and a computational points of view
Jia, Xiaodong. "Data Suitability Assessment and Enhancement for Machine Prognostics and Health Management Using Maximum Mean Discrepancy." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1544002523636343.
Повний текст джерелаOskarsson, Joel. "Probabilistic Regression using Conditional Generative Adversarial Networks." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166637.
Повний текст джерелаYang, Qibo. "A Transfer Learning Methodology of Domain Generalization for Prognostics and Health Management." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613749034966366.
Повний текст джерелаAtta-Asiamah, Ernest. "Distributed Inference for Degenerate U-Statistics with Application to One and Two Sample Test." Diss., North Dakota State University, 2020. https://hdl.handle.net/10365/31777.
Повний текст джерелаMayo, Thomas Richard. "Machine learning for epigenetics : algorithms for next generation sequencing data." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/33055.
Повний текст джерелаEbert, Anthony C. "Dynamic queueing networks: Simulation, estimation and prediction." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/180771/1/Anthony_Ebert_Thesis.pdf.
Повний текст джерелаRahman, Mohammad Mahfujur. "Deep domain adaptation and generalisation." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/205619/1/Mohammad%20Mahfujur_Rahman_Thesis.pdf.
Повний текст джерелаGupta, Yash. "Model Extraction Defense using Modified Variational Autoencoder." Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4430.
Повний текст джерелаDiu, Michael. "Image Analysis Applications of the Maximum Mean Discrepancy Distance Measure." Thesis, 2013. http://hdl.handle.net/10012/7558.
Повний текст джерелаЧастини книг з теми "Maximum Mean Discrepancy (MMD)"
Slimene, Alya, and Ezzeddine Zagrouba. "Kernel Maximum Mean Discrepancy for Region Merging Approach." In Computer Analysis of Images and Patterns, 475–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40246-3_59.
Повний текст джерелаDiu, Michael, Mehrdad Gangeh, and Mohamed S. Kamel. "Unsupervised Visual Changepoint Detection Using Maximum Mean Discrepancy." In Lecture Notes in Computer Science, 336–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39094-4_38.
Повний текст джерелаYang, Pengcheng, Fuli Luo, Shuangzhi Wu, Jingjing Xu, and Dongdong Zhang. "Learning Unsupervised Word Mapping via Maximum Mean Discrepancy." In Natural Language Processing and Chinese Computing, 290–302. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32233-5_23.
Повний текст джерелаLuna-Naranjo, D. F., J. V. Hurtado-Rincon, D. Cárdenas-Peña, V. H. Castro, H. F. Torres, and G. Castellanos-Dominguez. "EEG Channel Relevance Analysis Using Maximum Mean Discrepancy on BCI Systems." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 820–28. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13469-3_95.
Повний текст джерелаZhu, Xiaofeng, Kim-Han Thung, Ehsan Adeli, Yu Zhang, and Dinggang Shen. "Maximum Mean Discrepancy Based Multiple Kernel Learning for Incomplete Multimodality Neuroimaging Data." In Medical Image Computing and Computer Assisted Intervention − MICCAI 2017, 72–80. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66179-7_9.
Повний текст джерелаWickstrøm, Kristoffer, J. Emmanuel Johnson, Sigurd Løkse, Gustau Camps-Valls, Karl Øyvind Mikalsen, Michael Kampffmeyer, and Robert Jenssen. "The Kernelized Taylor Diagram." In Communications in Computer and Information Science, 125–31. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17030-0_10.
Повний текст джерелаТези доповідей конференцій з теми "Maximum Mean Discrepancy (MMD)"
Zhang, Wen, and Dongrui Wu. "Discriminative Joint Probability Maximum Mean Discrepancy (DJP-MMD) for Domain Adaptation." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9207365.
Повний текст джерелаXu, Zhiwei, Dapeng Li, Yunpeng Bai, and Guoliang Fan. "MMD-MIX: Value Function Factorisation with Maximum Mean Discrepancy for Cooperative Multi-Agent Reinforcement Learning." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533636.
Повний текст джерелаLiu, Qiao, and Hui Xue. "Adversarial Spectral Kernel Matching for Unsupervised Time Series Domain Adaptation." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/378.
Повний текст джерелаLi, Yanghao, Naiyan Wang, Jiaying Liu, and Xiaodi Hou. "Demystifying Neural Style Transfer." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/310.
Повний текст джерелаQian, Sheng, Guanyue Li, Wen-Ming Cao, Cheng Liu, Si Wu, and Hau San Wong. "Improving representation learning in autoencoders via multidimensional interpolation and dual regularizations." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/453.
Повний текст джерелаKim, Beomjoon, and Joelle Pineau. "Maximum Mean Discrepancy Imitation Learning." In Robotics: Science and Systems 2013. Robotics: Science and Systems Foundation, 2013. http://dx.doi.org/10.15607/rss.2013.ix.038.
Повний текст джерелаCai, Mingzhi, Baoguo Wei, Yue Zhang, Xu Li, and Lixin Li. "Maximum Mean Discrepancy Adversarial Active Learning." In 2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC). IEEE, 2022. http://dx.doi.org/10.1109/icspcc55723.2022.9984505.
Повний текст джерелаZhang, Wei, Brian Barr, and John Paisley. "Understanding Counterfactual Generation using Maximum Mean Discrepancy." In ICAIF '22: 3rd ACM International Conference on AI in Finance. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3533271.3561759.
Повний текст джерелаLin, Weiwei, Man-Wai Mak, Longxin Li, and Jen-Tzung Chien. "Reducing Domain Mismatch by Maximum Mean Discrepancy Based Autoencoders." In Odyssey 2018 The Speaker and Language Recognition Workshop. ISCA: ISCA, 2018. http://dx.doi.org/10.21437/odyssey.2018-23.
Повний текст джерелаTian, Yi, Qiuqi Ruan, and Gaoyun An. "Zero-shot Action Recognition via Empirical Maximum Mean Discrepancy." In 2018 14th IEEE International Conference on Signal Processing (ICSP). IEEE, 2018. http://dx.doi.org/10.1109/icsp.2018.8652306.
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