Search Results for author: Jing Tao

Found 7 papers, 2 papers with code

TBDLNet: a network for classifying multidrug-resistant and drug-sensitive tuberculosis

no code implementations27 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.


Multi-Action Dialog Policy Learning from Logged User Feedback

no code implementations27 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.

Federated Learning over Coupled Graphs

no code implementations26 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.

Federated Learning Node Classification

Doubly Robust Semiparametric Difference-in-Differences Estimators with High-Dimensional Data

no code implementations7 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.

valid Vocal Bursts Intensity Prediction

Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding

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.

General Classification Graph structure learning +3

MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions

2 code implementations23 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.

Video Rain Streak Removal by Multiscale Convolutional Sparse Coding

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.

Rain Removal

Cannot find the paper you are looking for? You can Submit a new open access paper.