8 citations · 18 across the 6 of their papers we have counts for
6 papers
Simple Yet Surprisingly Effective Training Strategies for LSTMs in Sensor-Based Human Activity Recognition
Shuai Shao, Yu Guan, Xin Guan +2
Human Activity Recognition (HAR) is one of the core research areas in mobile and wearable computing. With the application of deep learning (DL) techniques such as CNN, recognizing…
LSTA-Net: Long short-term Spatio-Temporal Aggregation Network for Skeleton-based Action Recognition
Tailin Chen, Shidong Wang, Desen Zhou +1
Modelling various spatio-temporal dependencies is the key to recognising human actions in skeleton sequences. Most existing methods excessively relied on the design of traversal ru…
Semi-Supervised Crowd Counting from Unlabeled Data
Haoran Duan, Fan Wan, Rui Sun +6
Automatic Crowd behavior analysis can be applied to effectively help the daily transportation statistics and planning, which helps the smart city construction. As one of the most i…
End to End ASR System with Automatic Punctuation Insertion
Yushi Guan
Recent Automatic Speech Recognition systems have been moving towards end-to-end systems that can be trained together. Numerous techniques that have been proposed recently enabled t…
Building Rule Hierarchies for Efficient Logical Rule Learning from Knowledge Graphs
Yulong Gu, Yu Guan, Paolo Missier
Many systems have been developed in recent years to mine logical rules from large-scale Knowledge Graphs (KGs), on the grounds that representing regularities as rules enables both…
Towards Learning Instantiated Logical Rules from Knowledge Graphs
Yulong Gu, Yu Guan, Paolo Missier
Efficiently inducing high-level interpretable regularities from knowledge graphs (KGs) is an essential yet challenging task that benefits many downstream applications. In this work…