1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2024
Adaptive Gradient Regularization: A Faster and Generalizable Optimization Technique for Deep Neural Networks
Huixiu Jiang, Ling Yang, Yu Bao +2
Stochastic optimization plays a crucial role in the advancement of deep learning technologies. Over the decades, significant effort has been dedicated to improving the training eff…
cs.LG2023★ 1 cited
Learning Stochastic Dynamical Systems as an Implicit Regularization with Graph Neural Networks
Jin Guo, Ting Gao, Yufu Lan +3
Stochastic Gumbel graph networks are proposed to learn high-dimensional time series, where the observed dimensions are often spatially correlated. To that end, the observed randomn…