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Everything about Tensor (machine learning)
Concept in machine learning
In machine learning, the term tensor informally refers to two different concepts: (i) a way of organizing data and (ii) a multilinear (tensor) transformation. Data may be organized in a multidimensional array (M-way array), informally referred to as a "data tensor"; however this shouldn't be confused with mathematical tensors, because data tensors are not invariant when a coordinate system is changed, for example when an image is rotated during data augmentation. Observations, such as images, movies, volumes, sounds, and relationships among words and concepts, stored in an M-way array ("data tensor"), may be analyzed either by artificial neural networks or tensor methods. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Tensor (machine learning)A 5-minute orientation
- Skim a recent paper: Presence and Absence of Barren Plateaus in Tensor-Network Based Machine LearningInfluential work since 2022
- Pick up a classic book: R Machine Learning ProjectsThe most-reprinted book on the subject
- Read the landmark paper: Tensor Decomposition for Signal Processing and Machine LearningCited 1,443 times
- Go sideways: Multilinear principal component analysisA closely related topic
- Go sideways: Multilinear subspace learningA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Tensor Decomposition for Signal Processing and Machine Learning
Towards quantum machine learning with tensor networks
Photonic tensor cores for machine learning
A Survey on Tensor Techniques and Applications in Machine Learning
Machine learning by unitary tensor network of hierarchical tree structure
Disentangling disorders of consciousness: Insights from diffusion tensor imaging and machine learning
Tensor-network-based machine learning of non-Markovian quantum processes
Spectroscopy from Machine Learning by Accurately Representing the Atomic Polar Tensor
🆕 Influential since 2022
Presence and Absence of Barren Plateaus in Tensor-Network Based Machine Learning
Diffusion Tensor Imaging in Amyotrophic Lateral Sclerosis: Machine Learning for Biomarker Development
Tensor networks for quantum machine learning
Tensor networks for unsupervised machine learning
Machine learning approach for obstructive sleep apnea screening using brain diffusion tensor imaging
📚 Books worth reading
- 📕R Machine Learning ProjectsSunil Kumar Chinnamgari · 2019
A TEXTBOOK OF TENSOR CALCULUSChaki, M. C. · 1987
Pro Deep Learning with TensorFlow: A Mathematical Approach to Advanced Artificial Intelligence in PythonSantanu Pattanayak · 2017
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Multilinear principal component analysis Multilinear principal component analysis (MPCA) is a multilinear extension of principal component analysis (PCA) that is used to analyze M-way arrays,…
- Multilinear subspace learning Multilinear subspace learning is an approach for disentangling the causal factor of data formation and performing dimensionality reduction. The Dimensionality…
- Tensor decomposition In multilinear algebra, a tensor decomposition is any scheme for expressing a "data tensor" (M-way array) as a sequence of elementary operations acting…
- Higher-order singular value decomposition In multilinear algebra, the higher-order singular value decomposition (HOSVD) is a misnomer. There does not exist a single tensor decomposition that retains…
- Multiway data analysis Multiway data analysis is a method of analyzing large data sets by representing a collection of observations as a multiway array, A ∈ C I 0 × I 1 × … I…
- Non-negative matrix factorization Non-negative matrix factorization (NMF or NNMF), also non-negative matrix approximation is a group of algorithms in multivariate analysis and linear algebra…
- Graph neural network Graph neural networks (GNNs) are artificial neural networks designed for tasks whose inputs are graphs. Because graphs usually do not have a canonical…
- Convolutional neural network A convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep…