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- W4310493238 abstract "Using tools from topology and functional analysis, we provide a framework where artificial neural networks, and their architectures, can be formally described. We define the notion of machine in a general topological context and show how simple machines can be combined into more complex ones. We explore finite- and infinite-depth machines, which generalize neural networks and neural ordinary differential equations. Borrowing ideas from functional analysis and kernel methods, we build complete, normed, infinite-dimensional spaces of machines, and we discuss how to find optimal architectures and parameters -- within those spaces -- to solve a given computational problem. In our numerical experiments, these kernel-inspired networks can outperform classical neural networks when the training dataset is small." @default.
- W4310493238 created "2022-12-11" @default.
- W4310493238 creator A5005388468 @default.
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- W4310493238 date "2020-07-06" @default.
- W4310493238 modified "2023-09-29" @default.
- W4310493238 title "Parametric machines: a fresh approach to architecture search" @default.
- W4310493238 doi "https://doi.org/10.48550/arxiv.2007.02777" @default.
- W4310493238 hasPublicationYear "2020" @default.
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