近日,电子科技大学信息与通信工程学院刘翼鹏副教授、刘佳妮博士生、龙珍博士生和朱策教授历时两年所著的《Tensor Computation for Data Analysis》由施普林格(Springer)出版集团正式出版。该书讨论了张量计算推广的一系列机器学习方法,详细讲解了张量计算基础,全方面多层次地介绍了张量计算方法在数据分析方面的各种应用,可以...
Independent component analysis finds latent variables that are statistically independent in observed data. The two related demos illustrate the computation of basic as well as constrained CPD. Read more Independent Vector Analysis Independent vector analysis is a multi-set extension of independent component...
This was done through an iterative computation process between (1) parameter inference in transition tensor models and (2) multiscale analysis of tensor-induced stochastic dynamical systems. Compared with the RNA velocity models, STT is unique in uncovering attractors underlying both the gene ...
Ahmadi-Asl S, Cichocki A, Phan AH, Asante-Mensah MG, Ghazani MM, Tanaka T, Oseledets IV (2020) Randomized algorithms for fast computation of low rank tensor ring model. Mach Learn Sci Technol 2(1):011001. https://doi.org/10.1088/2632-2153/abad87 Article Google Scholar Ahmadi-Asl ...
Processing data where it makes sense: enabling in-memory computation. Microprocess. Microsyst. 67, 28–41 (2019). Article Google Scholar Kang, M., Keel, M.-S., Shanbhag, N. R., Eilert, S. & Curewitz, K. An energy-efficient VLSI architecture for pattern recognition via deep embedding...
Kernels are tricky to use in data stream scenarios, as they require a computation of the whole Gram matrix, which is of size \(\mathcal {O}(N^2)\). In order to speed up the computations, one may use a random sampling of the input instances to create a new projected feature space....
The limited computation budget for the forward problem therefore leads to flattening of the high-dimensional landscape of the likelihoods, wiping off the structural information that should be used to navigate the optimisation algorithm towards the solution of (4). This motivates the development of ...
This was implemented to essentially identify the effect of considering three simplified particle dimensions in the characterisation of particle form, in contrast to a more detailed (yet computationally cheap) computation of the surface orientation tensor for the actual particle geometry. Since Eq. (11)...
Supervised learning with pair-input data has recently become one of the most intensively studied topics in pattern recognition literature, and its applications are numerous, including, for example, collaborative filtering, information retrieval, and drug
Tensor fields (matrix valued data sets) have recently attracted increased attention in the fields of image processing, computer vision, visualization and medical imaging. Tensor field segmentation is an important problem in tensor field analysis and has