Search Results for author: Wenzhong Guo

Found 20 papers, 3 papers with code

A novel particle swarm optimizer with multi-stage transformation and genetic operation for VLSI routing

no code implementations26 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.

Drug-Drug Interaction Prediction with Wasserstein Adversarial Autoencoder-based Knowledge Graph Embeddings

no code implementations15 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.

Knowledge Graph Embedding Knowledge Graph Embeddings +1

HDR-GAN: HDR Image Reconstruction from Multi-Exposed LDR Images with Large Motions

1 code implementation3 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.

HDR Reconstruction Image Reconstruction

Object Tracking by Least Spatiotemporal Searches

no code implementations18 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.

Management Object +1

Efficient Deep Embedded Subspace Clustering

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.

Clustering Deep Clustering +1

Enhance transferability of adversarial examples with model architecture

no code implementations28 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.

Wasserstein Adversarial Learning based Temporal Knowledge Graph Embedding

no code implementations4 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.

Knowledge Graph Embedding

Unsupervised Deep Discriminant Analysis Based Clustering

no code implementations9 Jun 2022 Jinyu Cai, Wenzhong Guo, Jicong Fan

This work presents an unsupervised deep discriminant analysis for clustering.

Clustering

MaskBlock: Transferable Adversarial Examples with Bayes Approach

1 code implementation13 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.

Defense against Backdoor Attacks via Identifying and Purifying Bad Neurons

no code implementations13 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.

backdoor defense

Learnable Graph Convolutional Network and Feature Fusion for Multi-view Learning

no code implementations16 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.

MULTI-VIEW LEARNING

Multi-view Graph Convolutional Networks with Differentiable Node Selection

no code implementations9 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.

Graph Embedding Graph Learning +1

Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Network

no code implementations5 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.

Clustering Graph Classification +3

Properties and Potential Applications of Random Functional-Linked Types of Neural Networks

no code implementations3 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.

Attributed Multi-order Graph Convolutional Network for Heterogeneous Graphs

no code implementations13 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.

Graph Learning

AGNN: Alternating Graph-Regularized Neural Networks to Alleviate Over-Smoothing

no code implementations14 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).

Graph Embedding

High-Similarity-Pass Attention for Single Image Super-Resolution

no code implementations25 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.

Image Super-Resolution

Bridging Trustworthiness and Open-World Learning: An Exploratory Neural Approach for Enhancing Interpretability, Generalization, and Robustness

no code implementations7 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.

FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Framework

no code implementations20 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.

Federated Learning Graph Anomaly Detection +1

ADEdgeDrop: Adversarial Edge Dropping for Robust Graph Neural Networks

no code implementations14 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.

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