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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.
ResNet may also refer to the department that administers such a network or the services provided via the network. A ResNet usually allows students to connect to a university's intranet with (possibly limited) access to the public Internet. ResNet is essentially the ISP for students residing in campus-managed housing.
The Home Energy Rating is an American estimated measurement of a home's energy efficiency based on normalized modified end-use loads (nMEULs). [1] In the United States, the Residential Energy Services Network (RESNET) is responsible for creation and maintenance of the RESNET Mortgage Industry National Home Energy Rating Standards (MINHERS), a proprietary system of standards, [2] which includes ...
He is an associate professor at Massachusetts Institute of Technology and is known as one of the creators of residual neural network (ResNet). [ 1 ] [ 3 ] Early life and education
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ResNet may refer to: Residential network, a computer network provided by a university to serve residence halls; Residual flow network, in graph theory; Residual neural network, a type of artificial neural network; Residential Energy Services Network (RESNET), an organization responsible for home energy ratings
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