This repository explores the concept of Orthogonal Gradient Descent (OGD) as a method to mitigate catastrophic forgetting in deep neural networks during continual learning scenarios. Catastrophic ...
Abstract: An attributed network is a network in which in addition to the network structure each node is associated with a set of attributes. Community detection in such networks involves recovering ...
With dams removed from the Klamath River, a group of Indigenous youth is on a journey to descend the full length, through Oregon and California. A year after four major dams were removed from the ...
ABSTRACT: In this paper, we consider a more general bi-level optimization problem, where the inner objective function is consisted of three convex functions, involving a smooth and two non-smooth ...
Differentially Private Stochastic Gradient Descent (DP-SGD) is a key method for training machine learning models like neural networks while ensuring privacy. It modifies the standard gradient descent ...
Adam is widely used in deep learning as an adaptive optimization algorithm, but it struggles with convergence unless the hyperparameter β2 is adjusted based on the specific problem. Attempts to fix ...
Abstract: Real-time minimization of line loss is a great challenge for conventional distributed control methods in medium-voltage DC distribution system (MVDC-DS), which may lead to low efficiency and ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results