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A residual neural network (also referred to as a residual network or ResNet) [1] is a deep learning architecture in which the layers learn residual functions with reference to the layer inputs. It was developed in 2015 for image recognition , and won the ImageNet Large Scale Visual Recognition Challenge ( ILSVRC ) of that year.
Every residual connection is a skip connection, but almost all skip connections are not residual connections. The original Highway Network paper [ 16 ] not only introduced the basic principle for very deep feedforward networks, but also included experimental results with 20, 50, and 100 layers networks, and mentioned ongoing experiments with up ...
In computing, a crash, or system crash, occurs when a computer program such as a software application or an operating system stops functioning properly and exits. On some operating systems or individual applications, a crash reporting service will report the crash and any details relating to it (or give the user the option to do so), usually to ...
This is in contrast to package decay-induced soft errors, which do not change with location. [5] As chip density increases, Intel expects the errors caused by cosmic rays to increase and become a limiting factor in design. [4] The average rate of cosmic-ray soft errors is inversely proportional to sunspot activity.
The user will only see the blue screen if the system is not configured to automatically restart (which became the default setting in Windows XP SP2). Otherwise, it appears as though the system simply rebooted (though a blue screen may be visible briefly). In Windows, bug checks are only supported by the Windows NT kernel.
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Error-correcting codes are used in lower-layer communication such as cellular network, high-speed fiber-optic communication and Wi-Fi, [11] [12] as well as for reliable storage in media such as flash memory, hard disk and RAM. [13] Error-correcting codes are usually distinguished between convolutional codes and block codes: