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- W4320342905 abstract "Deep Neural Networks (DNNs) are widely used for their ability to effectively approximate large classes of functions. This flexibility, however, makes the strict enforcement of constraints on DNNs an open problem. Here we present a framework that, under mild assumptions, allows the exact enforcement of constraints on parameterized sets of functions such as DNNs. Instead of imposing soft'' constraints via additional terms in the loss, we restrict (a subset of) the DNN parameters to a submanifold on which the constraints are satisfied exactly throughout the entire training procedure. We focus on constraints that are outside the scope of equivariant networks used in Geometric Deep Learning. As a major example of the framework, we restrict filters of a Convolutional Neural Network (CNN) to be wavelets, and apply these wavelet networks to the task of contour prediction in the medical domain." @default.
- W4320342905 created "2023-02-13" @default.
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- W4320342905 date "2023-02-09" @default.
- W4320342905 modified "2023-09-23" @default.
- W4320342905 title "Constrained Empirical Risk Minimization: Theory and Practice" @default.
- W4320342905 doi "https://doi.org/10.48550/arxiv.2302.04729" @default.
- W4320342905 hasPublicationYear "2023" @default.
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