Search Results for author: Yihua Huang

Found 10 papers, 5 papers with code

Spec-Gaussian: Anisotropic View-Dependent Appearance for 3D Gaussian Splatting

no code implementations24 Feb 2024 ZiYi Yang, Xinyu Gao, Yangtian Sun, Yihua Huang, Xiaoyang Lyu, Wen Zhou, Shaohui Jiao, Xiaojuan Qi, Xiaogang Jin

The recent advancements in 3D Gaussian splatting (3D-GS) have not only facilitated real-time rendering through modern GPU rasterization pipelines but have also attained state-of-the-art rendering quality.

Simple and Efficient Partial Graph Adversarial Attack: A New Perspective

1 code implementation15 Aug 2023 Guanghui Zhu, Mengyu Chen, Chunfeng Yuan, Yihua Huang

To this end, we propose a totally new method named partial graph attack (PGA), which selects the vulnerable nodes as attack targets.

Adversarial Attack

HAGNN: Hybrid Aggregation for Heterogeneous Graph Neural Networks

no code implementations4 Jul 2023 Guanghui Zhu, Zhennan Zhu, Hongyang Chen, Chunfeng Yuan, Yihua Huang

Then, we propose a novel framework to utilize the rich type semantic information in heterogeneous graphs comprehensively, namely HAGNN (Hybrid Aggregation for Heterogeneous GNNs).

Link Prediction Node Classification +1

AutoAC: Towards Automated Attribute Completion for Heterogeneous Graph Neural Network

1 code implementation8 Jan 2023 Guanghui Zhu, Zhennan Zhu, Wenjie Wang, Zhuoer Xu, Chunfeng Yuan, Yihua Huang

Moreover, to improve the performance of the downstream graph learning task, attribute completion and the training of the heterogeneous GNN should be jointly optimized rather than viewed as two separate processes.

Attribute Graph Learning +2

A2: Efficient Automated Attacker for Boosting Adversarial Training

1 code implementation7 Oct 2022 Zhuoer Xu, Guanghui Zhu, Changhua Meng, Shiwen Cui, ZhenZhe Ying, Weiqiang Wang, Ming Gu, Yihua Huang

In this paper, we propose an efficient automated attacker called A2 to boost AT by generating the optimal perturbations on-the-fly during training.

Adversarial Defense

Knowledge-enhanced Black-box Attacks for Recommendations

no code implementations21 Jul 2022 Jingfan Chen, Wenqi Fan, Guanghui Zhu, Xiangyu Zhao, Chunfeng Yuan, Qing Li, Yihua Huang

Recent studies have shown that deep neural networks-based recommender systems are vulnerable to adversarial attacks, where attackers can inject carefully crafted fake user profiles (i. e., a set of items that fake users have interacted with) into a target recommender system to achieve malicious purposes, such as promote or demote a set of target items.

Attribute Recommendation Systems

Transition Relation Aware Self-Attention for Session-based Recommendation

no code implementations12 Mar 2022 Guanghui Zhu, Haojun Hou, Jingfan Chen, Chunfeng Yuan, Yihua Huang

Specifically, TRASA first converts the session to a graph and then encodes the shortest path between items through the gated recurrent unit as their transition relation.

Relation Session-Based Recommendations

Multiscale Mesh Deformation Component Analysis with Attention-based Autoencoders

no code implementations4 Dec 2020 Jie Yang, Lin Gao, Qingyang Tan, Yihua Huang, Shihong Xia, Yu-Kun Lai

The attention mechanism is designed to learn to softly weight multi-scale deformation components in active deformation regions, and the stacked attention-based autoencoder is learned to represent the deformation components at different scales.

DIFER: Differentiable Automated Feature Engineering

1 code implementation17 Oct 2020 Guanghui Zhu, Zhuoer Xu, Xu Guo, Chunfeng Yuan, Yihua Huang

Extensive experiments on classification and regression datasets demonstrate that DIFER can significantly improve the performance of various machine learning algorithms and outperform current state-of-the-art AutoFE methods in terms of both efficiency and performance.

Automated Feature Engineering BIG-bench Machine Learning +2

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