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stat.ML2026
Towards a mathematical theory of superposition
Michael I. Ivanitskiy, John Jasper, Emily J. King +1
We develop a mathematical theory of superposition in neural networks using tools from frame theory and compressed sensing. In our model, a sparse binary vector \(x\) of active feat…
stat.ML2022
The Flag Median and FlagIRLS
Nathan Mankovich, Emily King, Chris Peterson +1
Finding prototypes (e.g., mean and median) for a dataset is central to a number of common machine learning algorithms. Subspaces have been shown to provide useful, robust represent…
stat.ML2020
Nonclosedness of Sets of Neural Networks in Sobolev Spaces
Scott Mahan, Emily King, Alex Cloninger
We examine the closedness of sets of realized neural networks of a fixed architecture in Sobolev spaces. For an exactly -times differentiable activation function , we constru…