11 citations · 33 across the 6 of their papers we have counts for
6 papers
Large-batch Optimization for Dense Visual Predictions
Zeyue Xue, Jianming Liang, Guanglu Song +4
Training a large-scale deep neural network in a large-scale dataset is challenging and time-consuming. The recent breakthrough of large-batch optimization is a promising way to tac…
Enhanced DeepONet for Modeling Partial Differential Operators Considering Multiple Input Functions
Lesley Tan, Liang Chen
Machine learning, especially deep learning is gaining much attention due to the breakthrough performance in various cognitive applications. Recently, neural networks (NN) have been…
Physics-Enforced Modeling for Insertion Loss of Transmission Lines by Deep Neural Networks
Liang Chen, Lesley Tan
In this paper, we investigate data-driven parameterized modeling of insertion loss for transmission lines with respect to design parameters. We first show that direct application o…
Topology Optimization through Differentiable Finite Element Solver
Liang Chen, Herman M. H. Shen
In this paper, a topology optimization framework utilizing automatic differentiation is presented as an efficient way for solving 2D density-based topology optimization problem by…
Variance-reduced Language Pretraining via a Mask Proposal Network
Liang Chen
Self-supervised learning, a.k.a., pretraining, is important in natural language processing. Most of the pretraining methods first randomly mask some positions in a sentence and the…
A New CGAN Technique for Constrained Topology Design Optimization
M. -H. Herman Shen, Liang Chen
This paper presents a new conditional GAN (named convex relaxing CGAN or crCGAN) to replicate the conventional constrained topology optimization algorithms in an extremely effectiv…