Search Results for author:
Found 5 papers, 2 papers with code
Towards understanding deep learning with the natural clustering prior
Hence, this thesis attempts to identify implicit clustering abilities, mechanisms and hyperparameters in deep learning systems and evaluate their relevance for explaining the generalization abilities of these systems.
Layer rotation: a surprisingly simple indicator of generalization in deep networks?
Our work presents empirical evidence that layer rotation, i. e. the evolution across training of the cosine distance between each layer's weight vector and its initialization, constitutes an impressively consistent indicator of generalization performance.
An experimental study of layer-level training speed and its impact on generalization
How optimization influences the generalization ability of a DNN is still an active area of research.
Layer rotation: a surprisingly powerful indicator of generalization in deep networks?
Our work presents extensive empirical evidence that layer rotation, i. e. the evolution across training of the cosine distance between each layer's weight vector and its initialization, constitutes an impressively consistent indicator of generalization performance.
Discovering the mechanics of hidden neurons
Neural networks trained through stochastic gradient descent (SGD) have been around for more than 30 years, but they still escape our understanding.
