168 citations · 265 across the 16 of their papers we have counts for
16 papers
Perturbation-Based Two-Stage Multi-Domain Active Learning
Rui He, Zeyu Dai, Shan He +1
In multi-domain learning (MDL) scenarios, high labeling effort is required due to the complexity of collecting data from various domains. Active Learning (AL) presents an encouragi…
Multi-Domain Learning From Insufficient Annotations
Rui He, Shengcai Liu, Jiahao Wu +2
Multi-domain learning (MDL) refers to simultaneously constructing a model or a set of models on datasets collected from different domains. Conventional approaches emphasize domain-…
Causality-driven Hierarchical Structure Discovery for Reinforcement Learning
Shaohui Peng, Xing Hu, Rui Zhang +9
Hierarchical reinforcement learning (HRL) effectively improves agents' exploration efficiency on tasks with sparse reward, with the guide of high-quality hierarchical structures (e…
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…