Search Results for author:
Found 2 papers, 0 papers with code
Demystify Optimization and Generalization of Over-parameterized PAC-Bayesian Learning
In addition to being a pure generalization bound analysis tool, PAC-Bayesian bound can also be incorporated into an objective function to train a probabilistic neural network, making them a powerful and relevant framework that can numerically provide a tight generalization bound for supervised learning.
Implicit bias of deep linear networks in the large learning rate phase
We claim that depending on the separation conditions of data, the gradient descent iterates will converge to a flatter minimum in the catapult phase.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
