Search Results for author: Haoyang Li

Found 24 papers, 10 papers with code

Out-of-Distribution Generalized Dynamic Graph Neural Network with Disentangled Intervention and Invariance Promotion

no code implementations24 Nov 2023 Zeyang Zhang, Xin Wang, Ziwei Zhang, Haoyang Li, Wenwu Zhu

In this paper, we propose Disentangled Intervention-based Dynamic graph Attention networks with Invariance Promotion (I-DIDA) to handle spatio-temporal distribution shifts in dynamic graphs by discovering and utilizing invariant patterns, i. e., structures and features whose predictive abilities are stable across distribution shifts.

Graph Attention

LLM4DyG: Can Large Language Models Solve Problems on Dynamic Graphs?

no code implementations26 Oct 2023 Zeyang Zhang, Xin Wang, Ziwei Zhang, Haoyang Li, Yijian Qin, Simin Wu, Wenwu Zhu

Our main observations are: 1) LLMs have preliminary spatial-temporal understanding abilities on dynamic graphs, 2) Dynamic graph tasks show increasing difficulties for LLMs as the graph size and density increase, while not sensitive to the time span and data generation mechanism, 3) the proposed DST2 prompting method can help to improve LLMs' spatial-temporal understanding abilities on dynamic graphs for most tasks.

Automated Bioinformatics Analysis via AutoBA

1 code implementation6 Sep 2023 Juexiao Zhou, Bin Zhang, Xiuying Chen, Haoyang Li, Xiaopeng Xu, Siyuan Chen, Xin Gao

With the fast-growing and evolving omics data, the demand for streamlined and adaptable tools to handle the analysis continues to grow.

Language Modelling Large Language Model

Graph Meets LLMs: Towards Large Graph Models

1 code implementation28 Aug 2023 Ziwei Zhang, Haoyang Li, Zeyang Zhang, Yijian Qin, Xin Wang, Wenwu Zhu

In order to promote applying large models for graphs forward, we present a perspective paper to discuss the challenges and opportunities associated with developing large graph models.

FISEdit: Accelerating Text-to-image Editing via Cache-enabled Sparse Diffusion Inference

no code implementations27 May 2023 Zihao Yu, Haoyang Li, Fangcheng Fu, Xupeng Miao, Bin Cui

The key intuition behind our approach is to utilize the semantic mapping between the minor modifications on the input text and the affected regions on the output image.

Audit to Forget: A Unified Method to Revoke Patients' Private Data in Intelligent Healthcare

1 code implementation20 Feb 2023 Juexiao Zhou, Haoyang Li, Xingyu Liao, Bin Zhang, Wenjia He, Zhongxiao Li, Longxi Zhou, Xin Gao

Revoking personal private data is one of the basic human rights, which has already been sheltered by several privacy-preserving laws in many countries.

Privacy Preserving

Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI

1 code implementation20 Feb 2023 Juexiao Zhou, Longxi Zhou, Di Wang, Xiaopeng Xu, Haoyang Li, Yuetan Chu, Wenkai Han, Xin Gao

However, there are few open-source frameworks for federated heterogeneous medical image analysis with personalization and privacy protection simultaneously without the demand to modify the existing model structures or to share any private data.

Privacy Preserving

RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL

1 code implementation12 Feb 2023 Haoyang Li, Jing Zhang, Cuiping Li, Hong Chen

Due to the structural property of the SQL queries, the seq2seq model takes the responsibility of parsing both the schema items (i. e., tables and columns) and the skeleton (i. e., SQL keywords).

Language Modelling Semantic Parsing +2

Curriculum Graph Machine Learning: A Survey

no code implementations6 Feb 2023 Haoyang Li, Xin Wang, Wenwu Zhu

To the best of our knowledge, this paper is the first survey for curriculum graph machine learning.

Model Optimization

FAIR AI Models in High Energy Physics

no code implementations9 Dec 2022 Javier Duarte, Haoyang Li, Avik Roy, Ruike Zhu, E. A. Huerta, Daniel Diaz, Philip Harris, Raghav Kansal, Daniel S. Katz, Ishaan H. Kavoori, Volodymyr V. Kindratenko, Farouk Mokhtar, Mark S. Neubauer, Sang Eon Park, Melissa Quinnan, Roger Rusack, Zhizhen Zhao

The findable, accessible, interoperable, and reusable (FAIR) data principles have provided a framework for examining, evaluating, and improving how we share data with the aim of facilitating scientific discovery.

Vocal Bursts Intensity Prediction

Unsupervised Visual Defect Detection with Score-Based Generative Model

no code implementations29 Nov 2022 Yapeng Teng, Haoyang Li, Fuzhen Cai, Ming Shao, Siyu Xia

Thus, we focus on the unsupervised visual defect detection and localization tasks and propose a novel framework based on the recent score-based generative models, which synthesize the real image by iterative denoising through stochastic differential equations (SDEs).

Anomaly Detection Defect Detection +1

Use Classifier as Generator

1 code implementation10 Sep 2022 Haoyang Li

Image recognition/classification is a widely studied problem, but its reverse problem, image generation, has drawn much less attention until recently.

Image Generation

Out-Of-Distribution Generalization on Graphs: A Survey

no code implementations16 Feb 2022 Haoyang Li, Xin Wang, Ziwei Zhang, Wenwu Zhu

This paper is the first systematic and comprehensive review of OOD generalization on graphs, to the best of our knowledge.

Out-of-Distribution Generalization

OOD-GNN: Out-of-Distribution Generalized Graph Neural Network

no code implementations7 Dec 2021 Haoyang Li, Xin Wang, Ziwei Zhang, Wenwu Zhu

Our proposed OOD-GNN employs a novel nonlinear graph representation decorrelation method utilizing random Fourier features, which encourages the model to eliminate the statistical dependence between relevant and irrelevant graph representations through iteratively optimizing the sample graph weights and graph encoder.

Out-of-Distribution Generalization

Disentangled Contrastive Learning on Graphs

no code implementations NeurIPS 2021 Haoyang Li, Xin Wang, Ziwei Zhang, Zehuan Yuan, Hang Li, Wenwu Zhu

Then we propose a novel factor-wise discrimination objective in a contrastive learning manner, which can force the factorized representations to independently reflect the expressive information from different latent factors.

Contrastive Learning Self-Supervised Learning

Noise Modulation: Let Your Model Interpret Itself

no code implementations19 Mar 2021 Haoyang Li, Xinggang Wang

Given the great success of Deep Neural Networks(DNNs) and the black-box nature of it, the interpretability of these models becomes an important issue. The majority of previous research works on the post-hoc interpretation of a trained model. But recently, adversarial training shows that it is possible for a model to have an interpretable input-gradient through training. However, adversarial training lacks efficiency for interpretability. To resolve this problem, we construct an approximation of the adversarial perturbations and discover a connection between adversarial training and amplitude modulation.

Quantitative Evaluations on Saliency Methods: An Experimental Study

no code implementations31 Dec 2020 Xiao-Hui Li, Yuhan Shi, Haoyang Li, Wei Bai, Yuanwei Song, Caleb Chen Cao, Lei Chen

It has been long debated that eXplainable AI (XAI) is an important topic, but it lacks rigorous definition and fair metrics.

Explainable Artificial Intelligence (XAI)

Billion-scale Network Embedding with Iterative Random Projection

2 code implementations7 May 2018 Ziwei Zhang, Peng Cui, Haoyang Li, Xiao Wang, Wenwu Zhu

Network embedding, which learns low-dimensional vector representation for nodes in the network, has attracted considerable research attention recently.

Distributed Computing Link Prediction +2

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