168 citations · 265 across the 16 of their papers we have counts for
6 papers · 1 filter
Training Quantized Deep Neural Networks via Cooperative Coevolution
Fu Peng, Shengcai Liu, Ning Lu +1
This work considers a challenging Deep Neural Network(DNN) quantization task that seeks to train quantized DNNs without involving any full-precision operations. Most previous quant…
An AI-assisted Economic Model of Endogenous Mobility and Infectious Diseases: The Case of COVID-19 in the United States
Lin William Cong, Ke Tang, Bing Wang +1
We build a deep-learning-based SEIR-AIM model integrating the classical Susceptible-Exposed-Infectious-Removed epidemiology model with forecast modules of infection, community mobi…
Efficient Combinatorial Optimization for Word-level Adversarial Textual Attack
Shengcai Liu, Ning Lu, Cheng Chen +1
Over the past few years, various word-level textual attack approaches have been proposed to reveal the vulnerability of deep neural networks used in natural language processing. Ty…
Multi-Domain Active Learning: Literature Review and Comparative Study
Rui He, Shengcai Liu, Shan He +1
Multi-domain learning (MDL) refers to learning a set of models simultaneously, where each model is specialized to perform a task in a particular domain. Generally, a high labeling…
Robust Dynamic Network Embedding via Ensembles
Chengbin Hou, Guoji Fu, Peng Yang +3
Dynamic Network Embedding (DNE) has recently attracted considerable attention due to the advantage of network embedding in various fields and the dynamic nature of many real-world…
A New Knowledge Gradient-based Method for Constrained Bayesian Optimization
Wenjie Chen, Shengcai Liu, Ke Tang
Black-box problems are common in real life like structural design, drug experiments, and machine learning. When optimizing black-box systems, decision-makers always consider multip…