231 citations · 703 across the 56 of their papers we have counts for
10 papers · 1 filter
ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning
Kun Wu, Yinuo Zhao, Zhiyuan Xu +5
Offline Reinforcement Learning (RL), which operates solely on static datasets without further interactions with the environment, provides an appealing alternative to learning a saf…
EPSD: Early Pruning with Self-Distillation for Efficient Model Compression
Dong Chen, Ning Liu, Yichen Zhu +6
Neural network compression techniques, such as knowledge distillation (KD) and network pruning, have received increasing attention. Recent work `Prune, then Distill' reveals that a…
CATRO: Channel Pruning via Class-Aware Trace Ratio Optimization
Wenzheng Hu, Zhengping Che, Ning Liu +4
Deep convolutional neural networks are shown to be overkill with high parametric and computational redundancy in many application scenarios, and an increasing number of works have…
Lottery Ticket Preserves Weight Correlation: Is It Desirable or Not?
Ning Liu, Geng Yuan, Zhengping Che +7
In deep model compression, the recent finding "Lottery Ticket Hypothesis" (LTH) (Frankle & Carbin, 2018) pointed out that there could exist a winning ticket (i.e., a properly prune…
Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control
Zhiyuan Xu, Kun Wu, Zhengping Che +2
While Deep Reinforcement Learning (DRL) has emerged as a promising approach to many complex tasks, it remains challenging to train a single DRL agent that is capable of undertaking…
D-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios
Zhengping Che, Guangyu Li, Tracy Li +7
Driving datasets accelerate the development of intelligent driving and related computer vision technologies, while substantial and detailed annotations serve as fuels and powers to…
Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets
Sanjay Purushotham, Chuizheng Meng, Zhengping Che +1
Deep learning models (aka Deep Neural Networks) have revolutionized many fields including computer vision, natural language processing, speech recognition, and is being increasingl…
Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records
Zhengping Che, Yu Cheng, Shuangfei Zhai +2
The rapid growth of Electronic Health Records (EHRs), as well as the accompanied opportunities in Data-Driven Healthcare (DDH), has been attracting widespread interests and attenti…
Exploiting Convolutional Neural Network for Risk Prediction with Medical Feature Embedding
Zhengping Che, Yu Cheng, Zhaonan Sun +1
The widespread availability of electronic health records (EHRs) promises to usher in the era of personalized medicine. However, the problem of extracting useful clinical representa…
Recurrent Neural Networks for Multivariate Time Series with Missing Values
Zhengping Che, Sanjay Purushotham, Kyunghyun Cho +2
Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values. In time series prediction a…