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- W2794674293 abstract "This article studies (multilayer perceptron) neural networks with an emphasis on the transformations involved --- both forward and backward --- in order to develop a semantical/logical perspective that is in line with standard program semantics. The common two-pass neural network training algorithms make this viewpoint particularly fitting. In the forward direction, neural networks act as state transformers. In the reverse direction, however, neural networks change losses of outputs to losses of inputs, thereby acting like a (real-valued) predicate transformer. In this way, backpropagation is functorial by construction, as shown earlier in recent other work. We illustrate this perspective by training a simple instance of a neural network." @default.
- W2794674293 created "2018-04-06" @default.
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- W2794674293 date "2018-03-25" @default.
- W2794674293 modified "2023-09-27" @default.
- W2794674293 title "Neural Nets via Forward State Transformation and Backward Loss Transformation" @default.
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- W2794674293 hasPublicationYear "2018" @default.
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