1 code implementation • 21 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.
no code implementations • 2 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.
no code implementations • 12 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.
no code implementations • 29 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.
no code implementations • 1 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.
no code implementations • 26 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.
no code implementations • 1 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.
no code implementations • 5 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.
no code implementations • 10 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.
no code implementations • 26 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.
no code implementations • 29 Sep 2021 • Xiaoling Zhou, Ou wu
Second, a flexible weighting scheme is proposed to overcome the defects of existing schemes.
no code implementations • 11 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.
no code implementations • 17 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.
1 code implementation • 16 May 2022 • Weiyao Zhu, Ou wu, Fengguang Su, Yingjun Deng
As learning difficulty is crucial for machine learning (e. g., difficulty-based weighting learning strategies), previous literature has proposed a number of learning difficulty measures.
1 code implementation • 13 Sep 2022 • Mengyang Li, Fengguang Su, Ou wu, Ji Zhang
However, limited studies have explicitly explored for the perturbation of logit vectors.
no code implementations • 12 Jan 2023 • Xiaoling Zhou, Ou wu, Weiyao Zhu, Ziyang Liang
In this study, we theoretically prove that the generalization error of a sample can be used as a universal difficulty measure.
no code implementations • 25 Apr 2023 • Xiaoling Zhou, Nan Yang, Ou wu
On the basis of our theoretical findings, a more general learning objective that combines adversaries and anti-adversaries with varied bounds on each training sample is presented.
no code implementations • 26 Apr 2023 • Xiaoling Zhou, Ou wu
Machine-learning models are prone to capturing the spurious correlations between non-causal attributes and classes, with counterfactual data augmentation being a promising direction for breaking these spurious associations.
no code implementations • 6 May 2023 • Ou wu
Imbalance learning is a subfield of machine learning that focuses on learning tasks in the presence of class imbalance.
1 code implementation • 25 Oct 2023 • Ou wu, Rujing Yao
Consequently, a huge number of studies in the existing literature have focused on the data aspect in deep learning tasks.