Researchers from the University of California San Diego have developed a mathematical formula that explains how neural networks learn and detect relevant patterns in data, providing insight into the mechanisms behind neural network learning and enabling improvements in machine learning efficiency.
Fourier features in learning systems like neural networks due to the downstream invariance of the learner that becomes insensitive to certain transformations, e.g., planar translation or rotation.
This article discusses cyclical encoding as an alternative to one-hot encoding for time series features in machine learning. Cyclical encoding provides the same information to the model with significantly fewer features.
