Literatura académica sobre el tema "Maximum Mean Discrepancy (MMD)"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "Maximum Mean Discrepancy (MMD)".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Artículos de revistas sobre el tema "Maximum Mean Discrepancy (MMD)"
Huang, Qihang, Yulin He y Zhexue Huang. "A Novel Maximum Mean Discrepancy-Based Semi-Supervised Learning Algorithm". Mathematics 10, n.º 1 (23 de diciembre de 2021): 39. http://dx.doi.org/10.3390/math10010039.
Texto completoZhou, Zhaokun, Yuanhong Zhong, Xiaoming Liu, Qiang Li y Shu Han. "DC-MMD-GAN: A New Maximum Mean Discrepancy Generative Adversarial Network Using Divide and Conquer". Applied Sciences 10, n.º 18 (14 de septiembre de 2020): 6405. http://dx.doi.org/10.3390/app10186405.
Texto completoXu, Haoji. "Generate Faces Using Ladder Variational Autoencoder with Maximum Mean Discrepancy (MMD)". Intelligent Information Management 10, n.º 04 (2018): 108–13. http://dx.doi.org/10.4236/iim.2018.104009.
Texto completoSun, Jiancheng. "Complex Network Construction of Univariate Chaotic Time Series Based on Maximum Mean Discrepancy". Entropy 22, n.º 2 (24 de enero de 2020): 142. http://dx.doi.org/10.3390/e22020142.
Texto completoZhang, Xiangqing, Yan Feng, Shun Zhang, Nan Wang, Shaohui Mei y Mingyi He. "Semi-Supervised Person Detection in Aerial Images with Instance Segmentation and Maximum Mean Discrepancy Distance". Remote Sensing 15, n.º 11 (4 de junio de 2023): 2928. http://dx.doi.org/10.3390/rs15112928.
Texto completoZhao, Ji y Deyu Meng. "FastMMD: Ensemble of Circular Discrepancy for Efficient Two-Sample Test". Neural Computation 27, n.º 6 (junio de 2015): 1345–72. http://dx.doi.org/10.1162/neco_a_00732.
Texto completoWilliamson, Sinead A. y Jette Henderson. "Understanding Collections of Related Datasets Using Dependent MMD Coresets". Information 12, n.º 10 (23 de septiembre de 2021): 392. http://dx.doi.org/10.3390/info12100392.
Texto completoLi, Kangji, Borui Wei, Qianqian Tang y Yufei Liu. "A Data-Efficient Building Electricity Load Forecasting Method Based on Maximum Mean Discrepancy and Improved TrAdaBoost Algorithm". Energies 15, n.º 23 (22 de noviembre de 2022): 8780. http://dx.doi.org/10.3390/en15238780.
Texto completoLee, Junghyun, Gwangsu Kim, Mahbod Olfat, Mark Hasegawa-Johnson y Chang D. Yoo. "Fast and Efficient MMD-Based Fair PCA via Optimization over Stiefel Manifold". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 7 (28 de junio de 2022): 7363–71. http://dx.doi.org/10.1609/aaai.v36i7.20699.
Texto completoHan, Chao, Deyun Zhou, Zhen Yang, Yu Xie y Kai Zhang. "Discriminative Sparse Filtering for Multi-Source Image Classification". Sensors 20, n.º 20 (16 de octubre de 2020): 5868. http://dx.doi.org/10.3390/s20205868.
Texto completoTesis sobre el tema "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.
Texto completoThis 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.
Texto completoOskarsson, 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.
Texto completoYang, 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.
Texto completoAtta-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.
Texto completoMayo, Thomas Richard. "Machine learning for epigenetics : algorithms for next generation sequencing data". Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/33055.
Texto completoEbert, 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.
Texto completoRahman, 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.
Texto completoGupta, Yash. "Model Extraction Defense using Modified Variational Autoencoder". Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4430.
Texto completoDiu, Michael. "Image Analysis Applications of the Maximum Mean Discrepancy Distance Measure". Thesis, 2013. http://hdl.handle.net/10012/7558.
Texto completoCapítulos de libros sobre el tema "Maximum Mean Discrepancy (MMD)"
Slimene, Alya y Ezzeddine Zagrouba. "Kernel Maximum Mean Discrepancy for Region Merging Approach". En 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.
Texto completoDiu, Michael, Mehrdad Gangeh y Mohamed S. Kamel. "Unsupervised Visual Changepoint Detection Using Maximum Mean Discrepancy". En 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.
Texto completoYang, Pengcheng, Fuli Luo, Shuangzhi Wu, Jingjing Xu y Dongdong Zhang. "Learning Unsupervised Word Mapping via Maximum Mean Discrepancy". En 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.
Texto completoLuna-Naranjo, D. F., J. V. Hurtado-Rincon, D. Cárdenas-Peña, V. H. Castro, H. F. Torres y G. Castellanos-Dominguez. "EEG Channel Relevance Analysis Using Maximum Mean Discrepancy on BCI Systems". En 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.
Texto completoZhu, Xiaofeng, Kim-Han Thung, Ehsan Adeli, Yu Zhang y Dinggang Shen. "Maximum Mean Discrepancy Based Multiple Kernel Learning for Incomplete Multimodality Neuroimaging Data". En 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.
Texto completoWickstrøm, Kristoffer, J. Emmanuel Johnson, Sigurd Løkse, Gustau Camps-Valls, Karl Øyvind Mikalsen, Michael Kampffmeyer y Robert Jenssen. "The Kernelized Taylor Diagram". En 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.
Texto completoActas de conferencias sobre el tema "Maximum Mean Discrepancy (MMD)"
Zhang, Wen y Dongrui Wu. "Discriminative Joint Probability Maximum Mean Discrepancy (DJP-MMD) for Domain Adaptation". En 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9207365.
Texto completoXu, Zhiwei, Dapeng Li, Yunpeng Bai y Guoliang Fan. "MMD-MIX: Value Function Factorisation with Maximum Mean Discrepancy for Cooperative Multi-Agent Reinforcement Learning". En 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533636.
Texto completoLiu, Qiao y Hui Xue. "Adversarial Spectral Kernel Matching for Unsupervised Time Series Domain Adaptation". En 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.
Texto completoLi, Yanghao, Naiyan Wang, Jiaying Liu y Xiaodi Hou. "Demystifying Neural Style Transfer". En 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.
Texto completoQian, Sheng, Guanyue Li, Wen-Ming Cao, Cheng Liu, Si Wu y Hau San Wong. "Improving representation learning in autoencoders via multidimensional interpolation and dual regularizations". En 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.
Texto completoKim, Beomjoon y Joelle Pineau. "Maximum Mean Discrepancy Imitation Learning". En Robotics: Science and Systems 2013. Robotics: Science and Systems Foundation, 2013. http://dx.doi.org/10.15607/rss.2013.ix.038.
Texto completoCai, Mingzhi, Baoguo Wei, Yue Zhang, Xu Li y Lixin Li. "Maximum Mean Discrepancy Adversarial Active Learning". En 2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC). IEEE, 2022. http://dx.doi.org/10.1109/icspcc55723.2022.9984505.
Texto completoZhang, Wei, Brian Barr y John Paisley. "Understanding Counterfactual Generation using Maximum Mean Discrepancy". En ICAIF '22: 3rd ACM International Conference on AI in Finance. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3533271.3561759.
Texto completoLin, Weiwei, Man-Wai Mak, Longxin Li y Jen-Tzung Chien. "Reducing Domain Mismatch by Maximum Mean Discrepancy Based Autoencoders". En Odyssey 2018 The Speaker and Language Recognition Workshop. ISCA: ISCA, 2018. http://dx.doi.org/10.21437/odyssey.2018-23.
Texto completoTian, Yi, Qiuqi Ruan y Gaoyun An. "Zero-shot Action Recognition via Empirical Maximum Mean Discrepancy". En 2018 14th IEEE International Conference on Signal Processing (ICSP). IEEE, 2018. http://dx.doi.org/10.1109/icsp.2018.8652306.
Texto completo