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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]
AlexNet is a convolutional neural network (CNN) architecture, designed by Alex Krizhevsky in collaboration with Ilya Sutskever and Geoffrey Hinton, who was Krizhevsky's Ph.D. advisor at the University of Toronto in 2012.
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.
U-Net is a convolutional neural network that was developed for image segmentation. [1] The network is based on a fully convolutional neural network [2] whose architecture was modified and extended to work with fewer training images and to yield more precise segmentation.
Convolutional neural networks are a kind of feed-forward neural network whose artificial neurons can respond to a part of the surrounding cells in the coverage range and perform well in large-scale image processing. LeNet-5 was one of the earliest convolutional neural networks and was historically important during the development of deep ...
The journal was established in 1988 and is published by Elsevier. [1] It covers all aspects of research on artificial neural networks. The founding editor-in-chief was Stephen Grossberg (Boston University). The current editors-in-chief are DeLiang Wang (Ohio State University) and Taro Toyoizumi (RIKEN Center for Brain Science).
Convolutional neural networks that have proven particularly successful in processing visual and other two-dimensional data; [154] [155] where long short-term memory avoids the vanishing gradient problem [156] and can handle signals that have a mix of low and high frequency components aiding large-vocabulary speech recognition, [157] [158] text ...
A neural network based three-state predictor based on both local and global features. Ranked in Top 5 based on AUC in CASP 9. Yes No CSpritz: 2011 Disorder definitions include: missing x-ray atoms (short) and DisProt style disorder (long). A probability of disorder is supplied with two decision thresholds which depend on the false positive rate.