903 citations · 947 across the 6 of their papers we have counts for
6 papers
Tensor Networks Meet Neural Networks: A Survey and Future Perspectives
Maolin Wang, Yu Pan, Zenglin Xu +4
Tensor networks (TNs) and neural networks (NNs) are two fundamental data modeling approaches. TNs were introduced to solve the curse of dimensionality in large-scale tensors by con…
Gradient Imitation Reinforcement Learning for General Low-Resource Information Extraction
Xuming Hu, Shiao Meng, Chenwei Zhang +4
Information Extraction (IE) aims to extract structured information from heterogeneous sources. IE from natural language texts include sub-tasks such as Named Entity Recognition (NE…
DeepVisualInsight: Time-Travelling Visualization for Spatio-Temporal Causality of Deep Classification Training
Xianglin Yang, Yun Lin, Ruofan Liu +4
Understanding how the predictions of deep learning models are formed during the training process is crucial to improve model performance and fix model defects, especially when we n…
AutoFT: Automatic Fine-Tune for Parameters Transfer Learning in Click-Through Rate Prediction
Xiangli Yang, Qing Liu, Rong Su +3
Recommender systems are often asked to serve multiple recommendation scenarios or domains. Fine-tuning a pre-trained CTR model from source domains and adapting it to a target domai…
A Survey on Deep Semi-supervised Learning
Xiangli Yang, Zixing Song, Irwin King +1
Deep semi-supervised learning is a fast-growing field with a range of practical applications. This paper provides a comprehensive survey on both fundamentals and recent advances in…
Graph-based Semi-supervised Learning: A Comprehensive Review
Zixing Song, Xiangli Yang, Zenglin Xu +1
Semi-supervised learning (SSL) has tremendous value in practice due to its ability to utilize both labeled data and unlabelled data. An important class of SSL methods is to natural…