Lock, E. F. (2017), `Tensor-on-tensor regression', arXiv preprint arXiv:1701.01037 .Eric F Lock. Tensor-on-tensor regression. arXiv preprint arXiv:1701.01037, 2017.Lock, E. F. (2018) Tensor-on-tensor regression. Journal of Computational and Graphical Statistics, 27, 638-647.Lock, E. F. (2017), `Tensor-on-tensor regression', Journal of C...
4. Linear tensor regression models 5. Nonlinear tensor regression ··· (更多) 丛书信息 ··· Foundations and Trends® in Machine Learning(共66册),这套丛书还有 《Metric Learning: A Survey》《Dynamical Variational Autoencoders: A Comprehensive Review》《Property Testing》《》《Learning with ...
(2017) High-dimensional adaptive function-on-scalar regression. Econometrics and Statistics, 1, 167–183. (Open in a new window)Google Scholar Fang, X., Paynabar, K. and Gebraeel, N. (2019) Image-based prognostics using penalized tensor regression. Technometrics, 61(3), 369–384. (...
Tensor regression sets up a multilinear mapping from a tensor input to a tensor output with a tensor system. As a tensor extension for linear regression, it has wide applications in various multidimensional data processing areas, such as weather forecasting, recommender systems, image prediction, bra...
Bayesian tensor regression. arxiv:1509.06490.Guhaniyogi, R., Qamar, S., and Dunson, D. B. (2017). Bayesian tensor regression. Journal of Machine Learning Research, 18(79):1-31. 32Rajarshi Guhaniyogi, Shaan Qamar, and David B Dunson. Bayesian tensor regression. arXiv preprint arXiv:...
In this paper, we study the tensor-variate regression problems and propose its nonparametric extension, which we break a nonlinear relationship on high-dimensional tensors into simple local functions with incorporating low-rank tensor decompositions. Compared with the naive nonparametric approach, our ...
pytorch多个tensor加权融合 pytorch tensor加法,1.基础知识torch.Tensor(a,b)#分配了a*b矩阵,只分配空间,未初始化torch.rand(a,b)#使用[0,1]均匀分布随机初始化二维数组torch.Size()#查看其形状torch对象相加的三种写法y.add_(x)#这种加法会改变y的值x+y#不会改变y的值re
线性回归假设自变量与因变量成线性关系,也就是 H(x)=Wx+b 由此关系我们构造cost function: cost=1m∑i=1m(H(x(i))−y(i))2H(x)=Wx+b 其中W和b被称为参数,故也可以写作 cost(W,b)=1m∑i=1m(H(x(i))−y(i))2 目标函数就是: ...
2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old. pythondata-sciencemachine-learningstatisticsresearchdeep-learningneural-networkgpuoptimizationscikit-learnpytorcheconometricsdata-analysistensorregression-modelsstatsmodels ...
这个源码是TensorFlow库中的一个名为`tensor_regression_data_algorithm`的函数,用于处理张量回归问题。张量回归是一种机器学习算法,用于解决多变量预测问题。在这个函数中,我们可以通过输入数据和目标值来计算模型的权重和偏置项。函数的主要步骤如下:1. 导入所需的库