no code implementations • 27 Oct 2023 • Ziquan Zhu, Jing Tao, Shuihua Wang, Xin Zhang, Yudong Zhang
Five indexes are selected in this paper, which are accuracy, sensitivity, precision, F1-score, and specificity.
no code implementations • 27 Feb 2023 • Shuo Zhang, Junzhou Zhao, Pinghui Wang, Tianxiang Wang, Zi Liang, Jing Tao, Yi Huang, Junlan Feng
To cope with this problem, we explore to improve multi-action dialog policy learning with explicit and implicit turn-level user feedback received for historical predictions (i. e., logged user feedback) that are cost-efficient to collect and faithful to real-world scenarios.
no code implementations • 26 Jan 2023 • Runze Lei, Pinghui Wang, Junzhou Zhao, Lin Lan, Jing Tao, Chao Deng, Junlan Feng, Xidian Wang, Xiaohong Guan
In this work, we propose a novel FL framework for graph data, FedCog, to efficiently handle coupled graphs that are a kind of distributed graph data, but widely exist in a variety of real-world applications such as mobile carriers' communication networks and banks' transaction networks.
no code implementations • 7 Sep 2020 • Yang Ning, Sida Peng, Jing Tao
This paper proposes a doubly robust two-stage semiparametric difference-in-difference estimator for estimating heterogeneous treatment effects with high-dimensional data.
1 code implementation • NeurIPS 2020 • Lin Lan, Pinghui Wang, Xuefeng Du, Kaikai Song, Jing Tao, Xiaohong Guan
We study the problem of node classification on graphs with few-shot novel labels, which has two distinctive properties: (1) There are novel labels to emerge in the graph; (2) The novel labels have only a few representative nodes for training a classifier.
2 code implementations • 23 May 2019 • Nuo Xu, Pinghui Wang, Long Chen, Jing Tao, Junzhou Zhao
To resolve these problems, we present MR-GNN, an end-to-end graph neural network with the following features: i) it uses a multi-resolution based architecture to extract node features from different neighborhoods of each node, and, ii) it uses dual graph-state long short-term memory networks (L-STMs) to summarize local features of each graph and extracts the interaction features between pairwise graphs.
no code implementations • CVPR 2018 • Minghan Li, Qi Xie, Qian Zhao, Wei Wei, Shuhang Gu, Jing Tao, Deyu Meng
Based on such understanding, we specifically formulate both characteristics into a multiscale convolutional sparse coding (MS-CSC) model for the video rain streak removal task.