Search Results for author: Yuan Fang

Found 54 papers, 17 papers with code

A Learned Generalized Geodesic Distance Function-Based Approach for Node Feature Augmentation on Graphs

no code implementations1 Jul 2024 Amitoz Azad, Yuan Fang

Geodesic distances on manifolds have numerous applications in image processing, computer graphics and computer vision.

Node Classification

Accelerating evolutionary exploration through language model-based transfer learning

no code implementations7 Jun 2024 Maximilian Reissmann, Yuan Fang, Andrew S. H. Ooi, Richard D. Sandberg

In this work, we propose an approach for integrating transfer learning with gene expression programming applied to symbolic regression.

Evolutionary Algorithms Language Modelling +3

A Practice-Friendly Two-Stage LLM-Enhanced Paradigm in Sequential Recommendation

no code implementations1 Jun 2024 Dugang Liu, Shenxian Xian, Xiaolin Lin, Xiaolian Zhang, Hong Zhu, Yuan Fang, Zhen Chen, Zhong Ming

The training paradigm integrating large language models (LLM) is gradually reshaping sequential recommender systems (SRS) and has shown promising results.

Sequential Recommendation

DyGPrompt: Learning Feature and Time Prompts on Dynamic Graphs

no code implementations22 May 2024 Xingtong Yu, Zhenghao Liu, Yuan Fang, Xinming Zhang

For dynamic graph modeling, dynamic graph neural networks (DGNNs) have emerged as a mainstream technique, which are generally pre-trained on the link prediction task, leaving a significant gap from the objectives of downstream tasks such as node classification.

Link Prediction Node Classification

Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models

no code implementations22 May 2024 Xingtong Yu, Chang Zhou, Yuan Fang, Xinming Zhang

To address these issues, we propose MDGPT, a text free Multi-Domain Graph Pre-Training and adaptation framework designed to exploit multi-domain knowledge for graph learning.

Graph Learning

Rethinking Scanning Strategies with Vision Mamba in Semantic Segmentation of Remote Sensing Imagery: An Experimental Study

no code implementations14 May 2024 Qinfeng Zhu, Yuan Fang, Yuanzhi Cai, Cheng Chen, Lei Fan

In this research, we conduct a comprehensive experimental investigation on the impact of mainstream scanning directions and their combinations on semantic segmentation of remotely sensed images.

Segmentation Segmentation Of Remote Sensing Imagery +1

Temporal and Heterogeneous Graph Neural Network for Remaining Useful Life Prediction

no code implementations7 May 2024 Zhihao Wen, Yuan Fang, Pengcheng Wei, Fayao Liu, Zhenghua Chen, Min Wu

To capture the nuances of the temporal and spatial relationships and heterogeneous characteristics in an interconnected graph of sensors, we introduce a novel model named Temporal and Heterogeneous Graph Neural Networks (THGNN).

Graph Neural Network

Collaborative-Enhanced Prediction of Spending on Newly Downloaded Mobile Games under Consumption Uncertainty

no code implementations12 Apr 2024 Peijie Sun, Yifan Wang, Min Zhang, Chuhan Wu, Yan Fang, Hong Zhu, Yuan Fang, Meng Wang

In summary, our contributions underscore the importance of stable model training frameworks and the efficacy of collaborative-enhanced models in predicting user spending behavior in mobile gaming.

Samba: Semantic Segmentation of Remotely Sensed Images with State Space Model

1 code implementation2 Apr 2024 Qinfeng Zhu, Yuanzhi Cai, Yuan Fang, Yihan Yang, Cheng Chen, Lei Fan, Anh Nguyen

The results reveal that Samba achieved unparalleled performance on commonly used remote sensing datasets for semantic segmentation.

Decoder Segmentation +1

Diffusion-based Negative Sampling on Graphs for Link Prediction

1 code implementation25 Mar 2024 Trung-Kien Nguyen, Yuan Fang

Furthermore, in the context of link prediction, most previous methods sample negative nodes from existing substructures of the graph, missing out on potentially more optimal samples in the latent space.

Link Prediction Recommendation Systems

Deep unfolding Network for Hyperspectral Image Super-Resolution with Automatic Exposure Correction

no code implementations14 Mar 2024 Yuan Fang, Yipeng Liu, Jie Chen, Zhen Long, Ao Li, Chong-Yung Chi, Ce Zhu

In recent years, the fusion of high spatial resolution multispectral image (HR-MSI) and low spatial resolution hyperspectral image (LR-HSI) has been recognized as an effective method for HSI super-resolution (HSI-SR).

Hyperspectral Image Super-Resolution Image Super-Resolution

SIBO: A Simple Booster for Parameter-Efficient Fine-Tuning

no code implementations19 Feb 2024 Zhihao Wen, Jie Zhang, Yuan Fang

Fine-tuning all parameters of large language models (LLMs) necessitates substantial computational power and extended time.

HGPROMPT: Bridging Homogeneous and Heterogeneous Graphs for Few-shot Prompt Learning

no code implementations4 Dec 2023 Xingtong Yu, Yuan Fang, Zemin Liu, Xinming Zhang

In this paper, we propose HGPROMPT, a novel pre-training and prompting framework to unify not only pre-training and downstream tasks but also homogeneous and heterogeneous graphs via a dual-template design.

Graph Representation Learning

MultiGPrompt for Multi-Task Pre-Training and Prompting on Graphs

1 code implementation28 Nov 2023 Xingtong Yu, Chang Zhou, Yuan Fang, Xinming Zhang

Hence, in this paper, we propose MultiGPrompt, a novel multi-task pre-training and prompting framework to exploit multiple pretext tasks for more comprehensive pre-trained knowledge.

General Knowledge Graph Representation Learning

Towards Graph Foundation Models: A Survey and Beyond

no code implementations18 Oct 2023 Jiawei Liu, Cheng Yang, Zhiyuan Lu, Junze Chen, Yibo Li, Mengmei Zhang, Ting Bai, Yuan Fang, Lichao Sun, Philip S. Yu, Chuan Shi

Foundation models have emerged as critical components in a variety of artificial intelligence applications, and showcase significant success in natural language processing and several other domains.

Graph Learning

Robust Long-Tailed Learning via Label-Aware Bounded CVaR

no code implementations29 Aug 2023 Hong Zhu, Runpeng Yu, Xing Tang, Yifei Wang, Yuan Fang, Yisen Wang

Data in the real-world classification problems are always imbalanced or long-tailed, wherein the majority classes have the most of the samples that dominate the model training.

Voucher Abuse Detection with Prompt-based Fine-tuning on Graph Neural Networks

no code implementations19 Aug 2023 Zhihao Wen, Yuan Fang, Yihan Liu, Yang Guo, Shuji Hao

We design a novel graph prompting function to reformulate the downstream task into a similar template as the pretext task in pre-training, thereby narrowing the objective gap.

Abuse Detection Anomaly Detection

OmniDataComposer: A Unified Data Structure for Multimodal Data Fusion and Infinite Data Generation

1 code implementation8 Aug 2023 Dongyang Yu, Shihao Wang, Yuan Fang, Wangpeng An

This paper presents OmniDataComposer, an innovative approach for multimodal data fusion and unlimited data generation with an intent to refine and uncomplicate interplay among diverse data modalities.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +6

Prompt Tuning on Graph-augmented Low-resource Text Classification

1 code implementation15 Jul 2023 Zhihao Wen, Yuan Fang

During pre-training, we propose three graph interaction-based contrastive strategies to jointly pre-train a graph-text model; during downstream classification, we explore handcrafted discrete prompts and continuous prompt tuning for the jointly pre-trained model to achieve zero- and few-shot classification, respectively.

Information Retrieval Retrieval +2

SDRCNN: A single-scale dense residual connected convolutional neural network for pansharpening

no code implementations1 Jul 2023 Yuan Fang, Yuanzhi Cai, Lei Fan

Pansharpening is a process of fusing a high spatial resolution panchromatic image and a low spatial resolution multispectral image to create a high-resolution multispectral image.


Contrastive Multi-view Framework for Customer Lifetime Value Prediction

no code implementations26 Jun 2023 Chuhan Wu, Jingjie Li, Qinglin Jia, Hong Zhu, Yuan Fang, Ruiming Tang

Accurate customer lifetime value (LTV) prediction can help service providers optimize their marketing policies in customer-centric applications.

Contrastive Learning Marketing +1

Encoding Enhanced Complex CNN for Accurate and Highly Accelerated MRI

no code implementations21 Jun 2023 Zimeng Li, Sa Xiao, Cheng Wang, Haidong Li, Xiuchao Zhao, Caohui Duan, Qian Zhou, Qiuchen Rao, Yuan Fang, Junshuai Xie, Lei Shi, Fumin Guo, Chaohui Ye, Xin Zhou

Magnetic resonance imaging (MRI) using hyperpolarized noble gases provides a way to visualize the structure and function of human lung, but the long imaging time limits its broad research and clinical applications.

MRI Reconstruction

OSTA: One-shot Task-adaptive Channel Selection for Semantic Segmentation of Multichannel Images

no code implementations8 May 2023 Yuanzhi Cai, Jagannath Aryal, Yuan Fang, Hong Huang, Lei Fan

In this study, the concept of pruning from a supernet is used for the first time to integrate the selection of channel combination and the training of a semantic segmentation network.

Segmentation Semantic Segmentation

Augmenting Low-Resource Text Classification with Graph-Grounded Pre-training and Prompting

no code implementations5 May 2023 Zhihao Wen, Yuan Fang

Text classification is a fundamental problem in information retrieval with many real-world applications, such as predicting the topics of online articles and the categories of e-commerce product descriptions.

Information Retrieval Retrieval +2

Link Prediction on Latent Heterogeneous Graphs

1 code implementation21 Feb 2023 Trung-Kien Nguyen, Zemin Liu, Yuan Fang

Assuming no type information is given, we define a so-called latent heterogeneous graph (LHG), which carries latent heterogeneous semantics as the node/edge types cannot be observed.

Link Prediction Representation Learning +1

GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks

2 code implementations16 Feb 2023 Zemin Liu, Xingtong Yu, Yuan Fang, Xinming Zhang

In particular, prompting is a popular alternative to fine-tuning in natural language processing, which is designed to narrow the gap between pre-training and downstream objectives in a task-specific manner.

Graph Representation Learning

A Survey on Spectral Graph Neural Networks

no code implementations11 Feb 2023 Deyu Bo, Xiao Wang, Yang Liu, Yuan Fang, Yawen Li, Chuan Shi

Graph neural networks (GNNs) have attracted considerable attention from the research community.

Graph Representation Learning

On Generalized Degree Fairness in Graph Neural Networks

1 code implementation8 Feb 2023 Zemin Liu, Trung-Kien Nguyen, Yuan Fang

In particular, the varying neighborhood structures across nodes, manifesting themselves in drastically different node degrees, give rise to the diverse behaviors of nodes and biased outcomes.

Fairness Graph Neural Network +1

Learning to Count Isomorphisms with Graph Neural Networks

1 code implementation7 Feb 2023 Xingtong Yu, Zemin Liu, Yuan Fang, Xinming Zhang

However, typical GNNs employ a node-centric message passing scheme that receives and aggregates messages on nodes, which is inadequate in complex structure matching for isomorphism counting.


VQNet 2.0: A New Generation Machine Learning Framework that Unifies Classical and Quantum

no code implementations9 Jan 2023 Huanyu Bian, Zhilong Jia, Menghan Dou, Yuan Fang, Lei LI, Yiming Zhao, Hanchao Wang, Zhaohui Zhou, Wei Wang, Wenyu Zhu, Ye Li, Yang Yang, Weiming Zhang, Nenghai Yu, Zhaoyun Chen, Guoping Guo

Therefore, based on VQNet 1. 0, we further propose VQNet 2. 0, a new generation of unified classical and quantum machine learning framework that supports hybrid optimization.

Quantum Machine Learning Unity

SBSS: Stacking-Based Semantic Segmentation Framework for Very High Resolution Remote Sensing Image

no code implementations15 Dec 2022 Yuanzhi Cai, Lei Fan, Yuan Fang

However, it was found in this study that different classes of objects had their preferred resizing scale for more accurate semantic segmentation.

Data Augmentation Segmentation +1

On the Probability of Necessity and Sufficiency of Explaining Graph Neural Networks: A Lower Bound Optimization Approach

no code implementations14 Dec 2022 Ruichu Cai, Yuxuan Zhu, Xuexin Chen, Yuan Fang, Min Wu, Jie Qiao, Zhifeng Hao

To address the non-identifiability of PNS, we resort to a lower bound of PNS that can be optimized via counterfactual estimation, and propose a framework of Necessary and Sufficient Explanation for GNN (NSEG) via optimizing that lower bound.


Motif Graph Neural Network

1 code implementation30 Dec 2021 Xuexin Chen, Ruichu Cai, Yuan Fang, Min Wu, Zijian Li, Zhifeng Hao

However, standard GNNs in the neighborhood aggregation paradigm suffer from limited discriminative power in distinguishing \emph{high-order} graph structures as opposed to \emph{low-order} structures.

Graph Classification Graph Embedding +3

Node-wise Localization of Graph Neural Networks

1 code implementation27 Oct 2021 Zemin Liu, Yuan Fang, Chenghao Liu, Steven C. H. Hoi

Ideally, how a node receives its neighborhood information should be a function of its local context, to diverge from the global GNN model shared by all nodes.

Representation Learning

Count-GNN: Graph Neural Networks for Subgraph Isomorphism Counting

no code implementations29 Sep 2021 Xingtong Yu, Zemin Liu, Yuan Fang, Xinming Zhang

At the graph level, we modulate the graph representation conditioned on the query subgraph, so that the model can be adapted to each unique query for better matching with the input graph.


Meta-Inductive Node Classification across Graphs

no code implementations14 May 2021 Zhihao Wen, Yuan Fang, Zemin Liu

That is, MI-GNN does not directly learn an inductive model; it learns the general knowledge of how to train a model for semi-supervised node classification on new graphs.

Classification General Knowledge +6

Growth, Electronic Structure and Superconductivity of Ultrathin Epitaxial CoSi2 Films

no code implementations21 Jan 2021 Yuan Fang, Ding Wang, Peng Li, Hang Su, Tian Le, Yi Wu, Guo-Wei Yang, Hua-Li Zhang, Zhi-Guang Xiao, Yan-Qiu Sun, Si-Yuan Hong, Yan-Wu Xie, Huan-Hua Wang, Chao Cao, Xin Lu, Hui-Qiu Yuan, Yang Liu

We report growth, electronic structure and superconductivity of ultrathin epitaxial CoSi2 films on Si(111).

Mesoscale and Nanoscale Physics

Localized Meta-Learning: A PAC-Bayes Analysis for Meta-Learning Beyond Global Prior

no code implementations1 Jan 2021 Chenghao Liu, Tao Lu, Doyen Sahoo, Yuan Fang, Kun Zhang, Steven Hoi

Meta-learning methods learn the meta-knowledge among various training tasks and aim to promote the learning of new tasks under the task similarity assumption.


Graph Deformer Network

no code implementations1 Jan 2021 Wenting Zhao, Yuan Fang, Zhen Cui, Tong Zhang, Jian Yang, Wei Liu

In this paper, we propose a simple yet effective graph deformer network (GDN) to fulfill anisotropic convolution filtering on graphs, analogous to the standard convolution operation on images.

Isomorphism Testing

Reflectivity and Spectrum of Relativistic Flying Plasma Mirrors

no code implementations10 Dec 2020 Yung-Kun Liu, Pisin Chen, Yuan Fang, Petr Valenta

Flying plasma mirrors induced by intense lasers has been proposed as a promising way to generate few-cycle EUV or X-ray lasers.

Plasma Physics

Charge density wave and weak Kondo effect in a Dirac semimetal CeSbTe

no code implementations23 Nov 2020 Peng Li, Baijiang Lv, Yuan Fang, Wei Guo, Zhongzheng Wu, Yi Wu, Cheng-Maw Cheng, Dawei Shen, Yuefeng Nie, Luca Petaccia, Chao Cao, Zhu-An Xu, Yang Liu

Using angle-resolved photoemission spectroscopy (ARPES) and low-energy electron diffraction (LEED), together with density-functional theory (DFT) calculation, we report the formation of charge density wave (CDW) and its interplay with the Kondo effect and topological states in CeSbTe.

Strongly Correlated Electrons Materials Science

Spatial Transformer Point Convolution

no code implementations3 Sep 2020 Yuan Fang, Chunyan Xu, Zhen Cui, Yuan Zong, Jian Yang

In this paper, we propose a spatial transformer point convolution (STPC) method to achieve anisotropic convolution filtering on point clouds.

Dictionary Learning Semantic Segmentation

Recent Advances in Network-based Methods for Disease Gene Prediction

1 code implementation19 Jul 2020 Sezin Kircali Ata, Min Wu, Yuan Fang, Le Ou-Yang, Chee Keong Kwoh, Xiao-Li Li

Thirdly, an empirical analysis is conducted to evaluate the performance of the selected methods across seven diseases.

Graph Representation Learning

Adaptive Task Sampling for Meta-Learning

no code implementations ECCV 2020 Chenghao Liu, Zhihao Wang, Doyen Sahoo, Yuan Fang, Kun Zhang, Steven C. H. Hoi

Meta-learning methods have been extensively studied and applied in computer vision, especially for few-shot classification tasks.

Classification General Classification +1

Emotion Recognition in Audio and Video Using Deep Neural Networks

2 code implementations15 Jun 2020 Mandeep Singh, Yuan Fang

Humans are able to comprehend information from multiple domains for e. g. speech, text and visual.

Multimodal Emotion Recognition speech-recognition +2

Multi-View Collaborative Network Embedding

3 code implementations17 May 2020 Sezin Kircali Ata, Yuan Fang, Min Wu, Jiaqi Shi, Chee Keong Kwoh, Xiao-Li Li

Real-world networks often exist with multiple views, where each view describes one type of interaction among a common set of nodes.

Diversity Network Embedding

Localized Meta-Learning: A PAC-Bayes Analysis for Meta-Leanring Beyond Global Prior

no code implementations25 Sep 2019 Chenghao Liu, Tao Lu, Doyen Sahoo, Yuan Fang, Steven C.H. Hoi.

Meta-learning methods learn the meta-knowledge among various training tasks and aim to promote the learning of new tasks under the task similarity assumption.


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