Search Results for author: Jiaqi Zhu

Found 13 papers, 3 papers with code

RulePrompt: Weakly Supervised Text Classification with Prompting PLMs and Self-Iterative Logical Rules

1 code implementation5 Mar 2024 Miaomiao Li, Jiaqi Zhu, Yang Wang, Yi Yang, Yilin Li, Hongan Wang

Weakly supervised text classification (WSTC), also called zero-shot or dataless text classification, has attracted increasing attention due to its applicability in classifying a mass of texts within the dynamic and open Web environment, since it requires only a limited set of seed words (label names) for each category instead of labeled data.

Pseudo Label text-classification +1

Representation Learning on Heterophilic Graph with Directional Neighborhood Attention

no code implementations3 Mar 2024 Qincheng Lu, Jiaqi Zhu, Sitao Luan, Xiao-Wen Chang

However, since it only incorporates information from immediate neighborhood, it lacks the ability to capture long-range and global graph information, leading to unsatisfactory performance on some datasets, particularly on heterophilic graphs.

Graph Attention Representation Learning

Balanced Multi-modal Federated Learning via Cross-Modal Infiltration

no code implementations31 Dec 2023 Yunfeng Fan, Wenchao Xu, Haozhao Wang, Jiaqi Zhu, Song Guo

Federated learning (FL) underpins advancements in privacy-preserving distributed computing by collaboratively training neural networks without exposing clients' raw data.

Distributed Computing Federated Learning +2

METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection

no code implementations28 Dec 2023 Jiaqi Zhu, Shaofeng Cai, Fang Deng, Beng Chin Ooi, Wenqiao Zhang

Real-time analytics and decision-making require online anomaly detection (OAD) to handle drifts in data streams efficiently and effectively.

Anomaly Detection Decision Making

On the Use of Singular Value Decomposition as a Clutter Filter for Ultrasound Flow Imaging

no code implementations25 Apr 2023 Kai Riemer, Marcelo Lerendegui, Matthieu Toulemonde, Jiaqi Zhu, Christopher Dunsby, Peter D. Weinberg, Meng-Xing Tang

Filtering based on Singular Value Decomposition (SVD) provides substantial separation of clutter, flow and noise in high frame rate ultrasound flow imaging.

Super-Resolution

When Do Graph Neural Networks Help with Node Classification? Investigating the Impact of Homophily Principle on Node Distinguishability

1 code implementation25 Apr 2023 Sitao Luan, Chenqing Hua, Minkai Xu, Qincheng Lu, Jiaqi Zhu, Xiao-Wen Chang, Jie Fu, Jure Leskovec, Doina Precup

Homophily principle, i. e., nodes with the same labels are more likely to be connected, has been believed to be the main reason for the performance superiority of Graph Neural Networks (GNNs) over Neural Networks on node classification tasks.

Node Classification Stochastic Block Model

DualMix: Unleashing the Potential of Data Augmentation for Online Class-Incremental Learning

no code implementations14 Mar 2023 Yunfeng Fan, Wenchao Xu, Haozhao Wang, Jiaqi Zhu, Junxiao Wang, Song Guo

Unfortunately, OCI learning can suffer from catastrophic forgetting (CF) as the decision boundaries for old classes can become inaccurate when perturbated by new ones.

Class Incremental Learning Data Augmentation +1

When Do We Need Graph Neural Networks for Node Classification?

no code implementations30 Oct 2022 Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Xiao-Wen Chang, Doina Precup

Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by additionally making use of graph structure based on the relational inductive bias (edge bias), rather than treating the nodes as collections of independent and identically distributed (i. i. d.)

Classification Inductive Bias +1

Revisiting Heterophily For Graph Neural Networks

1 code implementation14 Oct 2022 Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, Doina Precup

ACM is more powerful than the commonly used uni-channel framework for node classification tasks on heterophilic graphs and is easy to be implemented in baseline GNN layers.

Inductive Bias Node Classification on Non-Homophilic (Heterophilic) Graphs

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