Search Results for author: Xupeng Miao

Found 7 papers, 5 papers with code

ZOOMER: Boosting Retrieval on Web-scale Graphs by Regions of Interest

1 code implementation20 Mar 2022 Yuezihan Jiang, Yu Cheng, Hanyu Zhao, Wentao Zhang, Xupeng Miao, Yu He, Liang Wang, Zhi Yang, Bin Cui

We introduce ZOOMER, a system deployed at Taobao, the largest e-commerce platform in China, for training and serving GNN-based recommendations over web-scale graphs.

Dense-to-Sparse Gate for Mixture-of-Experts

1 code implementation29 Dec 2021 Xiaonan Nie, Shijie Cao, Xupeng Miao, Lingxiao Ma, Jilong Xue, Youshan Miao, Zichao Yang, Zhi Yang, Bin Cui

However, we found that the current approach of jointly training experts and the sparse gate introduces a negative impact on model accuracy, diminishing the efficiency of expensive large-scale model training.

PointCLIP: Point Cloud Understanding by CLIP

2 code implementations CVPR 2022 Renrui Zhang, Ziyu Guo, Wei zhang, Kunchang Li, Xupeng Miao, Bin Cui, Yu Qiao, Peng Gao, Hongsheng Li

On top of that, we design an inter-view adapter to better extract the global feature and adaptively fuse the few-shot knowledge learned from 3D into CLIP pre-trained in 2D.

Few-Shot Learning Transfer Learning

ROD: Reception-aware Online Distillation for Sparse Graphs

1 code implementation25 Jul 2021 Wentao Zhang, Yuezihan Jiang, Yang Li, Zeang Sheng, Yu Shen, Xupeng Miao, Liang Wang, Zhi Yang, Bin Cui

Unfortunately, many real-world networks are sparse in terms of both edges and labels, leading to sub-optimal performance of GNNs.

Graph Learning Knowledge Distillation +4

Memory-aware framework for fast and scalable second-order random walk over billion-edge natural graphs

no code implementations The VLDB Journal 2021 Yingxia Shao, Shiyue Huang, Yawen Li, Xupeng Miao, Bin Cui & Lei Chen

In this paper, to clearly compare the efficiency of various node sampling methods, we first design a cost model and propose two new node sampling methods: one follows the acceptance-rejection paradigm to achieve a better balance between memory and time cost, and the other is optimized for fast sampling the skewed probability distributions existed in natural graphs.

Community Detection Graph Embedding

DeGNN: Characterizing and Improving Graph Neural Networks with Graph Decomposition

no code implementations10 Oct 2019 Xupeng Miao, Nezihe Merve Gürel, Wentao Zhang, Zhichao Han, Bo Li, Wei Min, Xi Rao, Hansheng Ren, Yinan Shan, Yingxia Shao, Yujie Wang, Fan Wu, Hui Xue, Yaming Yang, Zitao Zhang, Yang Zhao, Shuai Zhang, Yujing Wang, Bin Cui, Ce Zhang

Despite the wide application of Graph Convolutional Network (GCN), one major limitation is that it does not benefit from the increasing depth and suffers from the oversmoothing problem.

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