Search Results for author:
Found 4 papers, 1 papers with code
Generalized Laplacian Regularized Framelet Graph Neural Networks
This paper introduces a novel Framelet Graph approach based on p-Laplacian GNN.
Adaptive Hierarchical Hyper-gradient Descent
In this study, we investigate learning rate adaption at different levels based on the hyper-gradient descent framework and propose a method that adaptively learns the optimizer parameters by combining multiple levels of learning rates with hierarchical structures.
Regularized Flexible Activation Function Combinations for Deep Neural Networks
Based on this, a novel family of flexible activation functions that can replace sigmoid or tanh in LSTM cells are implemented, as well as a new family by combining ReLU and ELUs.
COMBINED FLEXIBLE ACTIVATION FUNCTIONS FOR DEEP NEURAL NETWORKS
Based on this, we develop two novel flexible activation functions that can be implemented in LSTM cells and auto-encoder layers.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
