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Found 5 papers, 3 papers with code
Equivalence in Deep Neural Networks via Conjugate Matrix Ensembles
A numerical approach is developed for detecting the equivalence of deep learning architectures.
Periodic Spectral Ergodicity: A Complexity Measure for Deep Neural Networks and Neural Architecture Search
Establishing associations between the structure and the generalisation ability of deep neural networks (DNNs) is a challenging task in modern machine learning.
Generalised learning of time-series: Ornstein-Uhlenbeck processes
The performance of the primary models are evaluated simultaneously bycomputing the deviations from the originally removed data points and out-of-sample (OSS) data. Full cross-validation in time-series models can be practiced with rCV along with generating learning curves.
HARK Side of Deep Learning -- From Grad Student Descent to Automated Machine Learning
Recent advancements in machine learning research, i. e., deep learning, introduced methods that excel conventional algorithms as well as humans in several complex tasks, ranging from detection of objects in images and speech recognition to playing difficult strategic games.
Spectral Ergodicity in Deep Learning Architectures via Surrogate Random Matrices
It is observed that as the matrix size increases the level of spectral ergodicity of the ensemble rises, i. e., the eigenvalue spectra obtained for a single realisation at random from the ensemble is closer to the spectra obtained averaging over the whole ensemble.
