Search Results for author: Guiquan Liu

Found 4 papers, 0 papers with code

Hierarchy-Aware T5 with Path-Adaptive Mask Mechanism for Hierarchical Text Classification

no code implementations17 Sep 2021 Wei Huang, Chen Liu, Yihua Zhao, Xinyun Yang, Zhaoming Pan, Zhimin Zhang, Guiquan Liu

Hierarchical Text Classification (HTC), which aims to predict text labels organized in hierarchical space, is a significant task lacking in investigation in natural language processing.

Hierarchical structure Text Classification

Denoising User-aware Memory Network for Recommendation

no code implementations12 Jul 2021 Zhi Bian, Shaojun Zhou, Hao Fu, Qihong Yang, Zhenqi Sun, Junjie Tang, Guiquan Liu, Kaikui Liu, Xiaolong Li

Specifically, the framework: (i) proposes a feature purification module based on orthogonal mapping, which use the representation of explicit feedback to purify the representation of implicit feedback, and effectively denoise the implicit feedback; (ii) designs a user memory network to model the long-term interests in a fine-grained way by improving the memory network, which is ignored by the existing methods; and (iii) develops a preference-aware interactive representation component to fuse the long-term and short-term interests of users based on gating to understand the evolution of unbiased preferences of users.

Denoising

LRC-BERT: Latent-representation Contrastive Knowledge Distillation for Natural Language Understanding

no code implementations14 Dec 2020 Hao Fu, Shaojun Zhou, Qihong Yang, Junjie Tang, Guiquan Liu, Kaikui Liu, Xiaolong Li

In this work, we propose a knowledge distillation method LRC-BERT based on contrastive learning to fit the output of the intermediate layer from the angular distance aspect, which is not considered by the existing distillation methods.

Contrastive Learning Knowledge Distillation +2

Privacy Preserving PCA for Multiparty Modeling

no code implementations6 Feb 2020 Yingting Liu, Chaochao Chen, Longfei Zheng, Li Wang, Jun Zhou, Guiquan Liu, Shuang Yang

In this paper, we present a general multiparty modeling paradigm with Privacy Preserving Principal Component Analysis (PPPCA) for horizontally partitioned data.

Fraud Detection

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