1 code implementation • 13 Jan 2025 • Jie Tan, Yu Rong, Kangfei Zhao, Tian Bian, Tingyang Xu, Junzhou Huang, Hong Cheng, Helen Meng
Specifically, NLA-MMR formulates CMR as an alignment problem from patient and medication modalities.
no code implementations • 1 Mar 2024 • Jiaqi Han, Jiacheng Cen, Liming Wu, Zongzhao Li, Xiangzhe Kong, Rui Jiao, Ziyang Yu, Tingyang Xu, Fandi Wu, Zihe Wang, Hongteng Xu, Zhewei Wei, Yang Liu, Yu Rong, Wenbing Huang
Geometric graph is a special kind of graph with geometric features, which is vital to model many scientific problems.
no code implementations • 12 Feb 2024 • Haoyu Wang, Guozheng Ma, Ziqiao Meng, Zeyu Qin, Li Shen, Zhong Zhang, Bingzhe Wu, Liu Liu, Yatao Bian, Tingyang Xu, Xueqian Wang, Peilin Zhao
To further exploit the capabilities of bootstrapping, we investigate and adjust the training order of data, which yields improved performance of the model.
no code implementations • 18 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.
no code implementations • 25 Aug 2023 • Yang Liu, Jiashun Cheng, Haihong Zhao, Tingyang Xu, Peilin Zhao, Fugee Tsung, Jia Li, Yu Rong
Furthermore, we offer theoretical insights into SEGNO, highlighting that it can learn a unique trajectory between adjacent states, which is crucial for model generalization.
no code implementations • 21 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.
no code implementations • 14 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.
1 code implementation • 4 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.
no code implementations • 12 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.
no code implementations • 30 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.
no code implementations • 23 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.
no code implementations • 13 Sep 2022 • Lei Huang, Hengtong Zhang, Tingyang Xu, Ka-Chun Wong
At the same time, the generated molecules lack enough diversity.
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.
no code implementations • 23 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.
no code implementations • 20 May 2022 • Bingzhe Wu, Jintang Li, Junchi Yu, Yatao Bian, Hengtong Zhang, Chaochao Chen, Chengbin Hou, Guoji Fu, Liang Chen, Tingyang Xu, Yu Rong, Xiaolin Zheng, Junzhou Huang, Ran He, Baoyuan Wu, Guangyu Sun, Peng Cui, Zibin Zheng, Zhe Liu, Peilin Zhao
Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery.
no code implementations • 12 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.
2 code implementations • 31 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.
1 code implementation • 15 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.
Ranked #7 on
Semantic Segmentation
on SYNTHIA-to-Cityscapes
1 code implementation • 12 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.
1 code implementation • 3 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.
1 code implementation • 22 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.
1 code implementation • 17 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.
no code implementations • 15 Feb 2022 • Jiaqi Han, Yu Rong, Tingyang Xu, Wenbing Huang
Many scientific problems require to process data in the form of geometric graphs.
no code implementations • 25 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.
1 code implementation • 24 Jan 2022 • Yuanfeng Ji, Lu Zhang, Jiaxiang Wu, Bingzhe Wu, Long-Kai Huang, Tingyang Xu, Yu Rong, Lanqing Li, Jie Ren, Ding Xue, Houtim Lai, Shaoyong Xu, Jing Feng, Wei Liu, Ping Luo, Shuigeng Zhou, Junzhou Huang, Peilin Zhao, Yatao Bian
AI-aided drug discovery (AIDD) is gaining increasing popularity due to its promise of making the search for new pharmaceuticals quicker, cheaper and more efficient.
no code implementations • 18 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.
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.
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.
no code implementations • 29 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.
no code implementations • 29 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.
1 code implementation • 8 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.
1 code implementation • 14 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.
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.
no code implementations • 26 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.
no code implementations • 20 Mar 2021 • Junchi Yu, Tingyang Xu, Yu Rong, Yatao Bian, Junzhou Huang, Ran He
The emergence of Graph Convolutional Network (GCN) has greatly boosted the progress of graph learning.
no code implementations • 17 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.
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.
no code implementations • 4 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.
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.
no code implementations • 22 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.
no code implementations • 12 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.
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.
Ranked #4 on
Molecular Property Prediction
on QM7
no code implementations • 17 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.
no code implementations • 16 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.
1 code implementation • 4 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.
2 code implementations • 17 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.
1 code implementation • NeurIPS 2019 • Xingyu Cai, Tingyang Xu, Jin-Feng Yi, Junzhou Huang, Sanguthevar Rajasekaran
Dynamic Time Warping (DTW) is widely used as a similarity measure in various domains.
1 code implementation • 1 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.
no code implementations • 25 Sep 2019 • Kelong Mao, Peilin Zhao, Tingyang Xu, Yu Rong, Xi Xiao, Junzhou Huang
With massive possible synthetic routes in chemistry, retrosynthesis prediction is still a challenge for researchers.
Ranked #10 on
Single-step retrosynthesis
on USPTO-50k
1 code implementation • 4 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.
7 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.
Ranked #1 on
Node Classification
on Pubmed Full-supervised
no code implementations • 1 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).
no code implementations • CVPR 2019 • Chaoqi Chen, Weiping Xie, Wenbing Huang, Yu Rong, Xinghao Ding, Yue Huang, Tingyang Xu, Junzhou Huang
Unsupervised domain adaptation (UDA) transfers knowledge from a label-rich source domain to a fully-unlabeled target domain.
Ranked #8 on
Domain Adaptation
on SVHN-to-MNIST