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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.
A Unified Approach to Uniform Signal Recovery From Non-Linear Observations
An important characteristic of associated guarantees is uniformity, i. e., recovery succeeds for an entire class of structured signals with a fixed measurement ensemble.
Information Theory Information Theory Statistics Theory Statistics Theory
Quantized Compressed Sensing by Rectified Linear Units
This work is concerned with the problem of recovering high-dimensional signals $\mathbf{x} \in \mathbb{R}^n$ which belong to a convex set of low-complexity from a small number of quantized measurements.
Information Theory Information Theory Probability 62B10 G.3
