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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 ...
The process is subject to both burst errors and random errors. [7] Burst errors include those due to disc material (defects of aluminum reflecting film, poor reflective index of transparent disc material), disc production (faults during disc forming and disc cutting etc.), disc handling (scratches – generally thin, radial and orthogonal to ...
Low-density parity-check (LDPC) codes are a class of highly efficient linear block codes made from many single parity check (SPC) codes. They can provide performance very close to the channel capacity (the theoretical maximum) using an iterated soft-decision decoding approach, at linear time complexity in terms of their block length.
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In computer networking, a heterogeneous network is a network connecting computers and other devices where the operating systems and protocols have significant differences. For example, local area networks (LANs) that connect Windows, Linux and Macintosh computers are heterogeneous. [1] [2]
The Problem Reports and Solutions Control Panel applet was replaced by the Maintenance section of the Action Center on Windows 7 and Server 2008 R2.. A new app, Problem Steps Recorder (PSR.exe), is available on all builds of Windows 7 and enables the collection of the actions performed by a user while encountering a crash so that testers and developers can reproduce the situation for analysis ...
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