3 citations · 6 across the 6 of their papers we have counts for
6 papers
Combining Adam and its Inverse Counterpart to Enhance Generalization of Deep Learning Optimizers
Tao Shi, Liangming Chen, Long Jin +1
In the training of neural networks, adaptive moment estimation (Adam) typically converges fast but exhibits suboptimal generalization performance. A widely accepted explanation for…
A Method for Enhancing Generalization of Adam by Multiple Integrations
Long Jin, Han Nong, Liangming Chen +1
The insufficient generalization of adaptive moment estimation (Adam) has hindered its broader application. Recent studies have shown that flat minima in loss landscapes are highly…
Zero Stability Well Predicts Performance of Convolutional Neural Networks
Liangming Chen, Long Jin, Mingsheng Shang
The question of what kind of convolutional neural network (CNN) structure performs well is fascinating. In this work, we move toward the answer with one more step by connecting zer…
Activated Gradients for Deep Neural Networks
Mei Liu, Liangming Chen, Xiaohao Du +2
Deep neural networks often suffer from poor performance or even training failure due to the ill-conditioned problem, the vanishing/exploding gradient problem, and the saddle point…
Deforming the Loss Surface to Affect the Behaviour of the Optimizer
Liangming Chen, Long Jin, Xiujuan Du +2
In deep learning, it is usually assumed that the optimization process is conducted on a shape-fixed loss surface. Differently, we first propose a novel concept of deformation mappi…
Deforming the Loss Surface
Liangming Chen, Long Jin, Xiujuan Du +2
In deep learning, it is usually assumed that the shape of the loss surface is fixed. Differently, a novel concept of deformation operator is first proposed in this paper to deform…