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The codebase for AlexNet was released under a BSD license, and had been commonly used in neural network research for several subsequent years. [20] [17] In one direction, subsequent works aimed to train increasingly deep CNNs that achieve increasingly higher performance on ImageNet.
AlphaFold is a deep learning based system developed by DeepMind for prediction of protein structure. [77] Otter.ai is a speech-to-text synthesis and summary platform, which allows users to record online meetings as text. It additionally creates live captions during meetings. [78]
Provider Backbone Bridge Traffic Engineering was originally developed in 2006 as a Nortel specific protocol named Provider Backbone Transport (PBT). The company championed the technology and brought it to the IEEE 802.1 committee where it was renamed to PBB-TE and a working group, P802.1Qay, was chartered on May 7, 2007.
An extranet is a controlled private computer network that allows communication with business partners, vendors and suppliers or an authorized set of customers. It extends intranet to trusted outsiders. It provides access to needed services for authorized parties, without granting access to an organization's entire network.
For many years, sequence modelling and generation was done by using plain recurrent neural networks (RNNs). A well-cited early example was the Elman network (1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable ...
SqueezeNet is a deep neural network for image classification released in 2016. SqueezeNet was developed by researchers at DeepScale, University of California, Berkeley, and Stanford University. In designing SqueezeNet, the authors' goal was to create a smaller neural network with fewer parameters while achieving competitive accuracy.
Network engineering is rapidly evolving, driven by advancements in technology and new demands for connectivity. One trend is the integration of artificial intelligence (AI) and machine learning (ML) into network management. AI-powered tools are increasingly used for network automation and optimization, predictive analytics, and intelligent ...
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.