no code implementations • 26 Nov 2018 • Genggeng Liu, Zhen Zhuang, Wenzhong Guo, Naixue Xiong, Guolong Chen
Firstly, in order to be able to handle two types of SMT problems at the same time, an effective edge-vertex encoding strategy is proposed.
no code implementations • 15 Apr 2020 • Yuanfei Dai, Chenhao Guo, Wenzhong Guo, Carsten Eickhoff
Recently, several knowledge graph embedding approaches have received increasing attention in the DDI domain due to their capability of projecting drugs and interactions into a low-dimensional feature space for predicting links and classifying triplets.
1 code implementation • 3 Jul 2020 • Yuzhen Niu, Jianbin Wu, Wenxi Liu, Wenzhong Guo, Rynson W. H. Lau
To address these two problems, we propose in this paper a novel GAN-based model, HDR-GAN, for synthesizing HDR images from multi-exposed LDR images.
no code implementations • 18 Jul 2020 • Zhiyong Yu, Lei Han, Chao Chen, Wenzhong Guo, Zhiwen Yu
This paper proposes a strategy named IHMs (Intermediate Searching at Heuristic Moments): each step we figure out which moment is the best to search according to a heuristic indicator, then at that moment search locations one by one in descending order of predicted appearing probabilities, until a search hits; iterate this step until we get the object's current location.
1 code implementation • CVPR 2022 • Jinyu Cai, Jicong Fan, Wenzhong Guo, Shiping Wang, Yunhe Zhang, Zhao Zhang
The proposed method is out of the self-expressive framework, scales to the sample size linearly, and is applicable to arbitrarily large datasets and online clustering scenarios.
no code implementations • 28 Feb 2022 • Mingyuan Fan, Wenzhong Guo, Shengxing Yu, Zuobin Ying, Ximeng Liu
Transferability of adversarial examples is of critical importance to launch black-box adversarial attacks, where attackers are only allowed to access the output of the target model.
no code implementations • 4 May 2022 • Yuanfei Dai, Wenzhong Guo, Carsten Eickhoff
In order to deal with this issue, several temporal knowledge graph embedding (TKGE) approaches have been proposed to integrate temporal and structural information in recent years.
no code implementations • 9 Jun 2022 • Jinyu Cai, Wenzhong Guo, Jicong Fan
This work presents an unsupervised deep discriminant analysis for clustering.
1 code implementation • 13 Aug 2022 • Mingyuan Fan, Cen Chen, Ximeng Liu, Wenzhong Guo
By contrast, we re-formulate crafting transferable AEs as the maximizing a posteriori probability estimation problem, which is an effective approach to boost the generalization of results with limited available data.
no code implementations • 13 Aug 2022 • Mingyuan Fan, Yang Liu, Cen Chen, Ximeng Liu, Wenzhong Guo
The opacity of neural networks leads their vulnerability to backdoor attacks, where hidden attention of infected neurons is triggered to override normal predictions to the attacker-chosen ones.
no code implementations • 16 Nov 2022 • Zhaoliang Chen, Lele Fu, Jie Yao, Wenzhong Guo, Claudia Plant, Shiping Wang
In practical applications, multi-view data depicting objectives from assorted perspectives can facilitate the accuracy increase of learning algorithms.
no code implementations • 9 Dec 2022 • Zhaoliang Chen, Lele Fu, Shunxin Xiao, Shiping Wang, Claudia Plant, Wenzhong Guo
Due to the powerful capability to gather information of neighborhood nodes, in this paper, we apply Graph Convolutional Network (GCN) to cope with heterogeneous-graph data originating from multi-view data, which is still under-explored in the field of GCN.
no code implementations • 5 Feb 2023 • Jinyu Cai, Yi Han, Wenzhong Guo, Jicong Fan
In this work, we study the problem of partitioning a set of graphs into different groups such that the graphs in the same group are similar while the graphs in different groups are dissimilar.
no code implementations • 3 Apr 2023 • Guang-Yong Chen, Yong-Hang Yu, Min Gan, C. L. Philip Chen, Wenzhong Guo
Random functional-linked types of neural networks (RFLNNs), e. g., the extreme learning machine (ELM) and broad learning system (BLS), which avoid suffering from a time-consuming training process, offer an alternative way of learning in deep structure.
no code implementations • 13 Apr 2023 • Zhaoliang Chen, Zhihao Wu, Luying Zhong, Claudia Plant, Shiping Wang, Wenzhong Guo
Heterogeneous graph neural networks aim to discover discriminative node embeddings and relations from multi-relational networks. One challenge of heterogeneous graph learning is the design of learnable meta-paths, which significantly influences the quality of learned embeddings. Thus, in this paper, we propose an Attributed Multi-Order Graph Convolutional Network (AMOGCN), which automatically studies meta-paths containing multi-hop neighbors from an adaptive aggregation of multi-order adjacency matrices.
no code implementations • 14 Apr 2023 • Zhaoliang Chen, Zhihao Wu, Zhenghong Lin, Shiping Wang, Claudia Plant, Wenzhong Guo
In light of this, we propose an Alternating Graph-regularized Neural Network (AGNN) composed of Graph Convolutional Layer (GCL) and Graph Embedding Layer (GEL).
no code implementations • 25 May 2023 • Jian-Nan Su, Min Gan, Guang-Yong Chen, Wenzhong Guo, C. L. Philip Chen
Based on these findings, we introduced a concise yet effective soft thresholding operation to obtain high-similarity-pass attention (HSPA), which is beneficial for generating a more compact and interpretable distribution.
no code implementations • 7 Aug 2023 • Shide Du, Zihan Fang, Shiyang Lan, Yanchao Tan, Manuel Günther, Shiping Wang, Wenzhong Guo
As researchers strive to narrow the gap between machine intelligence and human through the development of artificial intelligence technologies, it is imperative that we recognize the critical importance of trustworthiness in open-world, which has become ubiquitous in all aspects of daily life for everyone.
no code implementations • 20 Feb 2024 • Jinyu Cai, Yunhe Zhang, Zhoumin Lu, Wenzhong Guo, See-Kiong Ng
Although federated learning offers a promising solution, the prevalent non-IID problems and high communication costs present significant challenges, particularly pronounced in collaborations with graph data distributed among different participants.
no code implementations • 14 Mar 2024 • Zhaoliang Chen, Zhihao Wu, Ylli Sadikaj, Claudia Plant, Hong-Ning Dai, Shiping Wang, Wenzhong Guo
Employing an adversarial training framework, the edge predictor utilizes the line graph transformed from the original graph to estimate the edges to be dropped, which improves the interpretability of the edge-dropping method.