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Found 5 papers, 1 papers with code
Memorization with neural nets: going beyond the worst case
In practice, deep neural networks are often able to easily interpolate their training data.
The Separation Capacity of Random Neural Networks
Neural networks with random weights appear in a variety of machine learning applications, most prominently as the initialization of many deep learning algorithms and as a computationally cheap alternative to fully learned neural networks.
Statistical post-processing of wind speed forecasts using convolutional neural networks
Current statistical post-processing methods for probabilistic weather forecasting are not capable of using full spatial patterns from the numerical weather prediction (NWP) model.
Dimensionality reduction with subgaussian matrices: a unified theory
We present a theory for Euclidean dimensionality reduction with subgaussian matrices which unifies several restricted isometry property and Johnson-Lindenstrauss type results obtained earlier for specific data sets.
Toward a unified theory of sparse dimensionality reduction in Euclidean space
Let $\Phi\in\mathbb{R}^{m\times n}$ be a sparse Johnson-Lindenstrauss transform [KN14] with $s$ non-zeroes per column.
