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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, in the strict mathematical sense, a tensor is a multilinear mapping over a set of domain vector spaces to a range vector ...
In artificial neural networks, a convolutional layer is a type of network layer that applies a convolution operation to the input. Convolutional layers are some of the primary building blocks of convolutional neural networks (CNNs), a class of neural network most commonly applied to images, video, audio, and other data that have the property of uniform translational symmetry.
A convolutional neural network (CNN) is a regularized type of feedforward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. [1]
LeNet has several common motifs of modern convolutional neural networks, such as convolutional layer, pooling layer and full connection layer. [3] Every convolutional layer includes three parts: convolution, pooling, and nonlinear activation functions; Using convolution to extract spatial features (Convolution was called receptive fields ...
The definition of a tensor as a multidimensional array satisfying a transformation law traces back to the work of Ricci. [1] An equivalent definition of a tensor uses the representations of the general linear group. There is an action of the general linear group on the set of all ordered bases of an n-dimensional vector space.
TuckER: [24] TuckER sees the knowledge graph as a tensor that could be decomposed using the Tucker decomposition in a collection of vectors—i.e., the embeddings of entities and relations—with a shared core. [24] [5] The weights of the core tensor are learned together with the embeddings and represent the level of interaction of the entries ...
In statistics, machine learning and algorithms, a tensor sketch is a type of dimensionality reduction that is particularly efficient when applied to vectors that have tensor structure. [ 1 ] [ 2 ] Such a sketch can be used to speed up explicit kernel methods , bilinear pooling in neural networks and is a cornerstone in many numerical linear ...
In image processing, a kernel, convolution matrix, or mask is a small matrix used for blurring, sharpening, embossing, edge detection, and more. This is accomplished by doing a convolution between the kernel and an image. Or more simply, when each pixel in the output image is a function of the nearby pixels (including itself) in the input image ...