123 citations · 202 across the 6 of their papers we have counts for
7 papers
One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation
Chenglin Li, Yuanzhen Xie, Chenyun Yu +5
Cross-domain recommendation is an important method to improve recommender system performance, especially when observations in target domains are sparse. However, most existing tech…
Mulco: Recognizing Chinese Nested Named Entities Through Multiple Scopes
Jiuding Yang, Jinwen Luo, Weidong Guo +3
Nested Named Entity Recognition (NNER) has been a long-term challenge to researchers as an important sub-area of Named Entity Recognition. NNER is where one entity may be part of a…
R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning
Shengyao Lu, Bang Liu, Keith G. Mills +2
Systematicity, i.e., the ability to recombine known parts and rules to form new sequences while reasoning over relational data, is critical to machine intelligence. A model with st…
LICHEE: Improving Language Model Pre-training with Multi-grained Tokenization
Weidong Guo, Mingjun Zhao, Lusheng Zhang +5
Language model pre-training based on large corpora has achieved tremendous success in terms of constructing enriched contextual representations and has led to significant performan…
Similarity Embedding Networks for Robust Human Activity Recognition
Chenglin Li, Carrie Lu Tong, Di Niu +5
Deep learning models for human activity recognition (HAR) based on sensor data have been heavily studied recently. However, the generalization ability of deep models on complex rea…
Meta-HAR: Federated Representation Learning for Human Activity Recognition
Chenglin Li, Di Niu, Bei Jiang +2
Human activity recognition (HAR) based on mobile sensors plays an important role in ubiquitous computing. However, the rise of data regulatory constraints precludes collecting priv…