Search Results for author: Ou wu

Found 12 papers, 1 papers with code

Tackling the Imbalance for GNNs

no code implementations17 Oct 2021 Rui Wang, Weixuan Xiong, Qinghu Hou, Ou wu

Different from deep neural networks for non-graph data classification, graph neural networks (GNNs) leverage the information exchange between nodes (or samples) when representing nodes.

Which Samples Should be Learned First: Easy or Hard?

no code implementations11 Oct 2021 Xiaoling Zhou, Ou wu

Factors including the distribution of samples' learning difficulties and the validation data determine which samples should be learned first in a learning task.

Compensation Learning

no code implementations26 Jul 2021 Rujing Yao, Ou wu

Furthermore, a family of new learning algorithms can be obtained by plugging the compensation learning into existing learning algorithms.

Graph Classification Image Classification +1

A Mathematical Foundation for Robust Machine Learning based on Bias-Variance Trade-off

no code implementations10 Jun 2021 Ou wu, Weiyao Zhu, Yingjun Deng, Haixiang Zhang, Qinghu Hou

Conducting a clear comparison for existing RML algorithms in dealing with different samples is difficult due to lack of a unified theoretical framework for RML.

Improving the Expressive Power of Graph Neural Network with Tinhofer Algorithm

no code implementations5 Apr 2021 Alan J. X. Guo, Qing-Hu Hou, Ou wu

In recent years, Graph Neural Network (GNN) has bloomly progressed for its power in processing graph-based data.

AI Marker-based Large-scale AI Literature Mining

no code implementations1 Nov 2020 Rujing Yao, Yingchun Ye, Ji Zhang, Shuxiao Li, Ou wu

Inspired by the idea of molecular markers tracing in the field of biochemistry, three named entities, namely, methods, datasets and metrics are used as AI markers for AI literature.

Literature Mining Saliency Detection

Method and Dataset Entity Mining in Scientific Literature: A CNN + Bi-LSTM Model with Self-attention

no code implementations26 Oct 2020 Linlin Hou, Ji Zhang, Ou wu, Ting Yu, Zhen Wang, Zhao Li, Jianliang Gao, Yingchun Ye, Rujing Yao

We finally apply our model on PAKDD papers published from 2009-2019 to mine insightful results from scientific papers published in a longer time span.

Data Augmentation

Deep Human Answer Understanding for Natural Reverse QA

no code implementations1 Dec 2019 Rujing Yao, Linlin Hou, Lei Yang, Jie Gui, Qing Yin, Ou wu

This study focuses on a reverse question answering (QA) procedure, in which machines proactively raise questions and humans supply the answers.

Question Answering

Method and Dataset Mining in Scientific Papers

no code implementations29 Nov 2019 Rujing Yao, Linlin Hou, Yingchun Ye, Ou wu, Ji Zhang, Jian Wu

In the field of machine learning, the involved methods (M) and datasets (D) are key information in papers.

Semi-interactive Attention Network for Answer Understanding in Reverse-QA

no code implementations12 Jan 2019 Qing Yin, Guan Luo, Xiaodong Zhu, QinGhua Hu, Ou wu

Question answering (QA) is an important natural language processing (NLP) task and has received much attention in academic research and industry communities.

Question Answering

Detecting Adversarial Examples via Key-based Network

no code implementations2 Jun 2018 Pinlong Zhao, Zhouyu Fu, Ou wu, QinGhua Hu, Jun Wang

In contrast to existing defense methods, the proposed method does not require knowledge of the process for generating adversarial examples and can be applied to defend against different types of attacks.

$ρ$-hot Lexicon Embedding-based Two-level LSTM for Sentiment Analysis

1 code implementation21 Mar 2018 Ou Wu, Tao Yang, Mengyang Li, Ming Li

Lexical cues are useful for sentiment analysis, and they have been utilized in conventional studies.

Sentiment Analysis

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