TensorLy: Tensor Learning in Python']Python

被引:0
作者
Kossaifi, Jean [1 ]
Panagakis, Yannis [1 ,2 ]
Anandkumar, Anima [3 ,4 ]
Pantic, Maja [1 ]
机构
[1] Imperial Coll London, London, England
[2] Middlesex Univ, London, England
[3] NVIDIA, Santa Clara, CA USA
[4] CALTECH, Pasadena, CA 91125 USA
关键词
DECOMPOSITIONS;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Tensors are higher-order extensions of matrices. While matrix methods form the cornerstone of traditional machine learning and data analysis, tensor methods have been gaining increasing traction. However, software support for tensor operations is not on the same footing. In order to bridge this gap, we have developed TensorLy, a Python library that provides a high-level API for tensor methods and deep tensorized neural networks. TensorLy aims to follow the same standards adopted by the main projects of the Python scientific community, and to seamlessly integrate with them. Its BSD license makes it suitable for both academic and commercial applications. TensorLy's backend system allows users to perform computations with several libraries such as NumPy or PyTorch to name but a few. They can be scaled on multiple CPU or GPU machines. In addition, using the deep-learning frameworks as backend allows to easily design and train deep tensorized neural networks. TensorLy is available at https://github.com/tensorly/tensorly
引用
收藏
页数:6
相关论文
共 27 条
[1]  
Abadi M., 2015, TENSORFLOW LARGESCAL
[2]   Unsupervised Multiway Data Analysis: A Literature Survey [J].
Acar, Evrim ;
Yener, Buelent .
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2009, 21 (01) :6-20
[3]  
Anandkumar A, 2014, J MACH LEARN RES, V15, P2773
[4]  
[Anonymous], 2017, CORR
[5]  
[Anonymous], SPECTRAL LEARNING MA
[6]  
[Anonymous], 2015, MXNET FLEXIBLE EFFIC
[7]  
[Anonymous], 2017, CORR
[8]  
[Anonymous], ARXIV160701668
[9]  
[Anonymous], CORR
[10]  
[Anonymous], 2009, NONNEGATIVE MATRIX T