Search Results for author: Yuhao Yang

Found 4 papers, 2 papers with code

Knowledge Graph Contrastive Learning for Recommendation

1 code implementation2 May 2022 Yuhao Yang, Chao Huang, Lianghao Xia, Chenliang Li

However, the success of such methods relies on the high quality knowledge graphs, and may not learn quality representations with two challenges: i) The long-tail distribution of entities results in sparse supervision signals for KG-enhanced item representation; ii) Real-world knowledge graphs are often noisy and contain topic-irrelevant connections between items and entities.

Contrastive Learning Knowledge Graphs +1

Eventor: An Efficient Event-Based Monocular Multi-View Stereo Accelerator on FPGA Platform

no code implementations29 Mar 2022 Mingjun Li, Jianlei Yang, Yingjie Qi, Meng Dong, Yuhao Yang, Runze Liu, Weitao Pan, Bei Yu, Weisheng Zhao

In this paper, Eventor is proposed as a fast and efficient EMVS accelerator by realizing the most critical and time-consuming stages including event back-projection and volumetric ray-counting on FPGA.


BigDL: A Distributed Deep Learning Framework for Big Data

2 code implementations16 Apr 2018 Jason Dai, Yiheng Wang, Xin Qiu, Ding Ding, Yao Zhang, Yanzhang Wang, Xianyan Jia, Cherry Zhang, Yan Wan, Zhichao Li, Jiao Wang, Shengsheng Huang, Zhongyuan Wu, Yang Wang, Yuhao Yang, Bowen She, Dongjie Shi, Qi Lu, Kai Huang, Guoqiong Song

This paper presents BigDL (a distributed deep learning framework for Apache Spark), which has been used by a variety of users in the industry for building deep learning applications on production big data platforms.

Fraud Detection Object Detection

Learning Social Circles in Ego Networks based on Multi-View Social Graphs

no code implementations16 Jul 2016 Chao Lan, Yuhao Yang, Xiao-Li Li, Bo Luo, Jun Huan

Based on extensive automatic and manual experimental evaluations, we deliver two major findings: first, multi-view clustering techniques perform better than common single-view clustering techniques, which only use one view or naively integrate all views for detection, second, the standard multi-view clustering technique is less robust than our modified technique, which selectively transfers information across views based on an assumption that sparse network structures are (potentially) incomplete.

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