Kernel methodsquantum information theoryWe consider the analysis of probability distributions through their associated covariance operators from reproducing kernel Hilbert spaces. We show that the von Neumann e
IEEE Transactions on Information Theory (1984) A. Hyvarinen et al. Independent Component Analysis (2001) A. Bell et al. An information-maximization approach to blind source separation and blind deconvolution Neural Computation (1995) J. Principe, J. Fisher, D. Xu, Information theoretic learning,...
In this work, we explored methods to improve the performance of cross-modal retrieval by integrating information theory and adversarial learning by analyzing the relation between information entropy and modality uncertainty. Based on this relation, we explored two different paradigms to combine information...
marquee seller site. We explain these effects using theory in multihoming and involvement, in terms of how reference prices are set. Our work extends the platforms literature by considering the specific influence of a marquee ...
communication happens in a Markovian fashion36. These two dimensions –information decay and memoryless transmission– are formally summarized by a fundamental principle of information theory, the data processing inequality (DPI)33, which we here apply to cross-species structural data and fMRI recordings...
1.刚从图书馆借到这本书,顺着书中的支持网站,发现作者把公开课视频也免费放到网上了,还可以直接下到英文原版电子版,这是什么精神~ ”A series of sixteen lectures covering the core of the book "Information Theory, Inference, and Learning Algorithms (Cambridge Un... (展开) ...
于是今天找到了一个lecture notes “The EM Algorithm: Theory, Applications and Related Methods”。不是什么太有名的教程,但是网上这方面的内容真的不多,又是一个成(guo)熟(qi)领域吗233. 但是这个教程里我今天看到的部分基本解决了我所有的疑惑,没解决的也当作疑惑在里面提出了,有如下几点: 首先,昨天说有...
Pattern recognition systems are trained using a finite number of samples. Feature dimensionality reduction is essential to improve generalization and optimal exploitation of the information content in the feature vector. Dimensionality reduction eliminat
We employ one of the largest quantum systems to date for a quantum machine learning experiment in this work with the single-shot setting that enables the use of a large quantum system. Results Kernel methods in machine learning In machine learning, one is asked to extract some patterns, or ...
Renyi, A.: Probability Theory. North-Holland Publishing Company, Amsterdam (1970) 18. Jenssen, R.: An Information Theoretic Approach to Machine Learning, PhD thesis, University of Troms, Department of Physics (2005) 19. Seo, S., Obermayer, K.: Soft learning vector quantization. Neural Computa...