Search Results for author: Xiaoyu Kou

Found 7 papers, 4 papers with code

r-GAT: Relational Graph Attention Network for Multi-Relational Graphs

no code implementations13 Sep 2021 Meiqi Chen, Yuan Zhang, Xiaoyu Kou, Yuntao Li, Yan Zhang

To tackle this issue, we propose r-GAT, a relational graph attention network to learn multi-channel entity representations.

Graph Attention Knowledge Graphs +1

DisenE: Disentangling Knowledge Graph Embeddings

no code implementations28 Oct 2020 Xiaoyu Kou, Yankai Lin, Yuntao Li, Jiahao Xu, Peng Li, Jie zhou, Yan Zhang

Knowledge graph embedding (KGE), aiming to embed entities and relations into low-dimensional vectors, has attracted wide attention recently.

Entity Embeddings Knowledge Graph Embedding +2

Disentangle-based Continual Graph Representation Learning

1 code implementation EMNLP 2020 Xiaoyu Kou, Yankai Lin, Shaobo Liu, Peng Li, Jie zhou, Yan Zhang

Graph embedding (GE) methods embed nodes (and/or edges) in graph into a low-dimensional semantic space, and have shown its effectiveness in modeling multi-relational data.

Continual Learning Graph Embedding +1

NASE: Learning Knowledge Graph Embedding for Link Prediction via Neural Architecture Search

1 code implementation18 Aug 2020 Xiaoyu Kou, Bingfeng Luo, Huang Hu, Yan Zhang

While various forms of models are proposed for the link prediction task, most of them are designed based on a few known relation patterns in several well-known datasets.

Knowledge Graph Embedding Link Prediction +1

Improving BERT with Self-Supervised Attention

1 code implementation8 Apr 2020 Yiren Chen, Xiaoyu Kou, Jiangang Bai, Yunhai Tong

One of the most popular paradigms of applying large pre-trained NLP models such as BERT is to fine-tune it on a smaller dataset.

Time-Series Anomaly Detection Service at Microsoft

3 code implementations10 Jun 2019 Hansheng Ren, Bixiong Xu, Yujing Wang, Chao Yi, Congrui Huang, Xiaoyu Kou, Tony Xing, Mao Yang, Jie Tong, Qi Zhang

At Microsoft, we develop a time-series anomaly detection service which helps customers to monitor the time-series continuously and alert for potential incidents on time.

Anomaly Detection Saliency Detection +1

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