Search Results for author: Tingyang Xu

Found 50 papers, 20 papers with code

Language Agents for Detecting Implicit Stereotypes in Text-to-image Models at Scale

no code implementations18 Oct 2023 Qichao Wang, Tian Bian, Yian Yin, Tingyang Xu, Hong Cheng, Helen M. Meng, Zibin Zheng, Liang Chen, Bingzhe Wu

The recent surge in the research of diffusion models has accelerated the adoption of text-to-image models in various Artificial Intelligence Generated Content (AIGC) commercial products.

Physics-Inspired Neural Graph ODE for Long-term Dynamical Simulation

no code implementations25 Aug 2023 Yang Liu, Jiashun Cheng, Haihong Zhao, Tingyang Xu, Peilin Zhao, Fugee Tsung, Jia Li, Yu Rong

In PINGO, to ensure the uniqueness of the trajectory, we construct a Physics-Inspired Neural ODE framework to update the latent trajectory.

Structure-Aware DropEdge Towards Deep Graph Convolutional Networks

no code implementations21 Jun 2023 Jiaqi Han, Wenbing Huang, Yu Rong, Tingyang Xu, Fuchun Sun, Junzhou Huang

Regarding the layer-dependent sampler, we interestingly find that increasingly sampling edges from the bottom layer yields superior performance than the decreasing counterpart as well as DropEdge.

Node Classification

Towards Controllable Diffusion Models via Reward-Guided Exploration

no code implementations14 Apr 2023 Hengtong Zhang, Tingyang Xu

By formulating data samples' formation as a Markov denoising process, diffusion models achieve state-of-the-art performances in a collection of tasks.

Denoising Reinforcement Learning (RL)

Decision Support System for Chronic Diseases Based on Drug-Drug Interactions

1 code implementation4 Mar 2023 Tian Bian, Yuli Jiang, Jia Li, Tingyang Xu, Yu Rong, Yi Su, Timothy Kwok, Helen Meng, Hong Cheng

Many patients with chronic diseases resort to multiple medications to relieve various symptoms, which raises concerns about the safety of multiple medication use, as severe drug-drug antagonism can lead to serious adverse effects or even death.

counterfactual Representation Learning

Human Mobility Modeling During the COVID-19 Pandemic via Deep Graph Diffusion Infomax

no code implementations12 Dec 2022 Yang Liu, Yu Rong, Zhuoning Guo, Nuo Chen, Tingyang Xu, Fugee Tsung, Jia Li

To address these challenges, we formulate the micro perspective mobility modeling into computing the relevance score between a diffusion and a location, conditional on a geometric graph.

Handling Missing Data via Max-Entropy Regularized Graph Autoencoder

no code implementations30 Nov 2022 Ziqi Gao, Yifan Niu, Jiashun Cheng, Jianheng Tang, Tingyang Xu, Peilin Zhao, Lanqing Li, Fugee Tsung, Jia Li

In this work, we present a regularized graph autoencoder for graph attribute imputation, named MEGAE, which aims at mitigating spectral concentration problem by maximizing the graph spectral entropy.


Towards Complete-View and High-Level Pose-based Gait Recognition

no code implementations23 Sep 2022 Honghu Pan, Yongyong Chen, Tingyang Xu, Yunqi He, Zhenyu He

Extensive experiments on two large gait recognition datasets, i. e., CASIA-B and OUMVLP-Pose, demonstrate that our method outperforms the baseline model and existing pose-based methods by a large margin.

Gait Recognition Vocal Bursts Intensity Prediction

Similarity-aware Positive Instance Sampling for Graph Contrastive Pre-training

no code implementations NeurIPS 2021 Xueyi Liu, Yu Rong, Tingyang Xu, Fuchun Sun, Wenbing Huang, Junzhou Huang

To remedy this issue, we propose to select positive graph instances directly from existing graphs in the training set, which ultimately maintains the legality and similarity to the target graphs.

Contrastive Learning Graph Classification +1

Tyger: Task-Type-Generic Active Learning for Molecular Property Prediction

no code implementations23 May 2022 Kuangqi Zhou, Kaixin Wang, Jiashi Feng, Jian Tang, Tingyang Xu, Xinchao Wang

However, existing best deep AL methods are mostly developed for a single type of learning task (e. g., single-label classification), and hence may not perform well in molecular property prediction that involves various task types.

Active Learning Drug Discovery +3

GPN: A Joint Structural Learning Framework for Graph Neural Networks

no code implementations12 May 2022 Qianggang Ding, Deheng Ye, Tingyang Xu, Peilin Zhao

To the best of our knowledge, our method is the first GNN-based bilevel optimization framework for resolving this task.

Bilevel Optimization

Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected Graphs

1 code implementation31 Mar 2022 Jiying Zhang, Fuyang Li, Xi Xiao, Tingyang Xu, Yu Rong, Junzhou Huang, Yatao Bian

As a powerful tool for modeling complex relationships, hypergraphs are gaining popularity from the graph learning community.

Graph Learning

Smoothing Matters: Momentum Transformer for Domain Adaptive Semantic Segmentation

1 code implementation15 Mar 2022 Runfa Chen, Yu Rong, Shangmin Guo, Jiaqi Han, Fuchun Sun, Tingyang Xu, Wenbing Huang

After the great success of Vision Transformer variants (ViTs) in computer vision, it has also demonstrated great potential in domain adaptive semantic segmentation.

Pseudo Label Segmentation +2

Equivariant Graph Mechanics Networks with Constraints

1 code implementation12 Mar 2022 Wenbing Huang, Jiaqi Han, Yu Rong, Tingyang Xu, Fuchun Sun, Junzhou Huang

The core of GMN is that it represents, by generalized coordinates, the forward kinematics information (positions and velocities) of a structural object.

Learning Neural Set Functions Under the Optimal Subset Oracle

1 code implementation3 Mar 2022 Zijing Ou, Tingyang Xu, Qinliang Su, Yingzhen Li, Peilin Zhao, Yatao Bian

Learning neural set functions becomes increasingly more important in many applications like product recommendation and compound selection in AI-aided drug discovery.

Anomaly Detection Drug Discovery +2

Equivariant Graph Hierarchy-Based Neural Networks

1 code implementation22 Feb 2022 Jiaqi Han, Wenbing Huang, Tingyang Xu, Yu Rong

Equivariant Graph neural Networks (EGNs) are powerful in characterizing the dynamics of multi-body physical systems.

Transformer for Graphs: An Overview from Architecture Perspective

1 code implementation17 Feb 2022 Erxue Min, Runfa Chen, Yatao Bian, Tingyang Xu, Kangfei Zhao, Wenbing Huang, Peilin Zhao, Junzhou Huang, Sophia Ananiadou, Yu Rong

In this survey, we provide a comprehensive review of various Graph Transformer models from the architectural design perspective.

Geometrically Equivariant Graph Neural Networks: A Survey

no code implementations15 Feb 2022 Jiaqi Han, Yu Rong, Tingyang Xu, Wenbing Huang

Many scientific problems require to process data in the form of geometric graphs.

Inductive Bias

Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer

no code implementations25 Jan 2022 Erxue Min, Yu Rong, Tingyang Xu, Yatao Bian, Peilin Zhao, Junzhou Huang, Da Luo, Kangyi Lin, Sophia Ananiadou

Although these methods have made great progress, they are often limited by the recommender system's direct exposure and inactive interactions, and thus fail to mine all potential user interests.

Click-Through Rate Prediction Representation Learning

Towards the Explanation of Graph Neural Networks in Digital Pathology with Information Flows

no code implementations18 Dec 2021 Junchi Yu, Tingyang Xu, Ran He

In this work, we address these key challenges and propose IFEXPLAINER, which generates a necessary and sufficient explanation for GNNs.

Not All Low-Pass Filters are Robust in Graph Convolutional Networks

1 code implementation NeurIPS 2021 Heng Chang, Yu Rong, Tingyang Xu, Yatao Bian, Shiji Zhou, Xin Wang, Junzhou Huang, Wenwu Zhu

Graph Convolutional Networks (GCNs) are promising deep learning approaches in learning representations for graph-structured data.

Weakly Supervised Graph Clustering

no code implementations29 Sep 2021 Tian Bian, Tingyang Xu, Yu Rong, Wenbing Huang, Xi Xiao, Peilin Zhao, Junzhou Huang, Hong Cheng

Graph Clustering, which clusters the nodes of a graph given its collection of node features and edge connections in an unsupervised manner, has long been researched in graph learning and is essential in certain applications.

Clustering Graph Clustering +1

PI-GNN: Towards Robust Semi-Supervised Node Classification against Noisy Labels

no code implementations29 Sep 2021 Xuefeng Du, Tian Bian, Yu Rong, Bo Han, Tongliang Liu, Tingyang Xu, Wenbing Huang, Junzhou Huang

Semi-supervised node classification on graphs is a fundamental problem in graph mining that uses a small set of labeled nodes and many unlabeled nodes for training, so that its performance is quite sensitive to the quality of the node labels.

Graph Mining Node Classification

Constrained Graph Mechanics Networks

no code implementations ICLR 2022 Wenbing Huang, Jiaqi Han, Yu Rong, Tingyang Xu, Fuchun Sun, Junzhou Huang

In this manner, the geometrical constraints are implicitly and naturally encoded in the forward kinematics.

Local Augmentation for Graph Neural Networks

1 code implementation8 Sep 2021 Songtao Liu, Rex Ying, Hanze Dong, Lanqing Li, Tingyang Xu, Yu Rong, Peilin Zhao, Junzhou Huang, Dinghao Wu

To address this, we propose a simple and efficient data augmentation strategy, local augmentation, to learn the distribution of the node features of the neighbors conditioned on the central node's feature and enhance GNN's expressive power with generated features.

Open-Ended Question Answering

Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions

1 code implementation14 Jun 2021 Xuefeng Du, Tian Bian, Yu Rong, Bo Han, Tongliang Liu, Tingyang Xu, Wenbing Huang, Yixuan Li, Junzhou Huang

This paper bridges the gap by proposing a pairwise framework for noisy node classification on graphs, which relies on the PI as a primary learning proxy in addition to the pointwise learning from the noisy node class labels.

Contrastive Learning Graph Learning +2

Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning

no code implementations ICLR 2022 Yatao Bian, Yu Rong, Tingyang Xu, Jiaxiang Wu, Andreas Krause, Junzhou Huang

By running fixed point iteration for multiple steps, we achieve a trajectory of the valuations, among which we define the valuation with the best conceivable decoupling error as the Variational Index.

Data Valuation Variational Inference

Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge

no code implementations26 May 2021 Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang, Peng Cui, Xin Wang, Wenwu Zhu, Junzhou Huang

We investigate the theoretical connections between graph signal processing and graph embedding models and formulate the graph embedding model as a general graph signal process with a corresponding graph filter.

Adversarial Attack Graph Embedding +1

Diversified Multiscale Graph Learning with Graph Self-Correction

no code implementations17 Mar 2021 Yuzhao Chen, Yatao Bian, Jiying Zhang, Xi Xiao, Tingyang Xu, Yu Rong, Junzhou Huang

Though the multiscale graph learning techniques have enabled advanced feature extraction frameworks, the classic ensemble strategy may show inferior performance while encountering the high homogeneity of the learnt representation, which is caused by the nature of existing graph pooling methods.

Ensemble Learning Graph Classification +1

Deep Multimodal Fusion by Channel Exchanging

1 code implementation NeurIPS 2020 Yikai Wang, Wenbing Huang, Fuchun Sun, Tingyang Xu, Yu Rong, Junzhou Huang

Deep multimodal fusion by using multiple sources of data for classification or regression has exhibited a clear advantage over the unimodal counterpart on various applications.

Image-to-Image Translation Semantic Segmentation +1

On Self-Distilling Graph Neural Network

no code implementations4 Nov 2020 Yuzhao Chen, Yatao Bian, Xi Xiao, Yu Rong, Tingyang Xu, Junzhou Huang

Furthermore, the inefficient training process of teacher-student knowledge distillation also impedes its applications in GNN models.

Graph Embedding Knowledge Distillation

Graph Information Bottleneck for Subgraph Recognition

1 code implementation ICLR 2021 Junchi Yu, Tingyang Xu, Yu Rong, Yatao Bian, Junzhou Huang, Ran He

In this paper, we propose a framework of Graph Information Bottleneck (GIB) for the subgraph recognition problem in deep graph learning.

Denoising Graph Classification +1

Tackling Over-Smoothing for General Graph Convolutional Networks

no code implementations22 Aug 2020 Wenbing Huang, Yu Rong, Tingyang Xu, Fuchun Sun, Junzhou Huang

Increasing the depth of GCN, which is expected to permit more expressivity, is shown to incur performance detriment especially on node classification.

Node Classification

Inverse Graph Identification: Can We Identify Node Labels Given Graph Labels?

no code implementations12 Jul 2020 Tian Bian, Xi Xiao, Tingyang Xu, Yu Rong, Wenbing Huang, Peilin Zhao, Junzhou Huang

Upon a formal discussion of the variants of IGI, we choose a particular case study of node clustering by making use of the graph labels and node features, with an assistance of a hierarchical graph that further characterizes the connections between different graphs.

Clustering Community Detection +3

Self-Supervised Graph Transformer on Large-Scale Molecular Data

3 code implementations NeurIPS 2020 Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying WEI, Wenbing Huang, Junzhou Huang

We pre-train GROVER with 100 million parameters on 10 million unlabelled molecules -- the biggest GNN and the largest training dataset in molecular representation learning.

Molecular Property Prediction molecular representation +2

Multi-View Graph Neural Networks for Molecular Property Prediction

no code implementations17 May 2020 Hehuan Ma, Yatao Bian, Yu Rong, Wenbing Huang, Tingyang Xu, Weiyang Xie, Geyan Ye, Junzhou Huang

Guided by this observation, we present Multi-View Graph Neural Network (MV-GNN), a multi-view message passing architecture to enable more accurate predictions of molecular properties.

Drug Discovery Molecular Property Prediction +1

Spectral Graph Attention Network with Fast Eigen-approximation

no code implementations16 Mar 2020 Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Somayeh Sojoudi, Junzhou Huang, Wenwu Zhu

In this paper, we first introduce the attention mechanism in the spectral domain of graphs and present Spectral Graph Attention Network (SpGAT) that learns representations for different frequency components regarding weighted filters and graph wavelets bases.

Graph Attention Node Classification +1

Graph Representation Learning via Graphical Mutual Information Maximization

1 code implementation4 Feb 2020 Zhen Peng, Wenbing Huang, Minnan Luo, Qinghua Zheng, Yu Rong, Tingyang Xu, Junzhou Huang

The richness in the content of various information networks such as social networks and communication networks provides the unprecedented potential for learning high-quality expressive representations without external supervision.

Graph Representation Learning Link Prediction +2

Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks

2 code implementations17 Jan 2020 Tian Bian, Xi Xiao, Tingyang Xu, Peilin Zhao, Wenbing Huang, Yu Rong, Junzhou Huang

Meanwhile, detecting rumors from such massive information in social media is becoming an arduous challenge.

Re-balancing Variational Autoencoder Loss for Molecule Sequence Generation

1 code implementation1 Oct 2019 Chaochao Yan, Sheng Wang, Jinyu Yang, Tingyang Xu, Junzhou Huang

We investigate the posterior collapse problem of current RNN-based VAEs for molecule sequence generation.


A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models

1 code implementation4 Aug 2019 Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang, Peng Cui, Wenwu Zhu, Junzhou Huang

To this end, we begin by investigating the theoretical connections between graph signal processing and graph embedding models in a principled way and formulate the graph embedding model as a general graph signal process with corresponding graph filter.

Adversarial Attack Graph Embedding +2

DropEdge: Towards Deep Graph Convolutional Networks on Node Classification

6 code implementations ICLR 2020 Yu Rong, Wenbing Huang, Tingyang Xu, Junzhou Huang

\emph{Over-fitting} and \emph{over-smoothing} are two main obstacles of developing deep Graph Convolutional Networks (GCNs) for node classification.

Classification General Classification +1

Unsupervised Adversarial Graph Alignment with Graph Embedding

no code implementations1 Jul 2019 Chaoqi Chen, Weiping Xie, Tingyang Xu, Yu Rong, Wenbing Huang, Xinghao Ding, Yue Huang, Junzhou Huang

In this paper, we propose an Unsupervised Adversarial Graph Alignment (UAGA) framework to learn a cross-graph alignment between two embedding spaces of different graphs in a fully unsupervised fashion (\emph{i. e.,} no existing anchor links and no users' personal profile or attribute information is available).

Graph Embedding Link Prediction

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