Search Results for author: Liyao Xiang

Found 18 papers, 2 papers with code

Context-Aware Compilation of DNN Training Pipelines across Edge and Cloud

1 code implementation Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2021 Dixi Yao, Liyao Xiang, Zifan Wang, Jiayu Xu, Chao Li, Xinbing Wang

Experimental results show that our system not only adapts well to, but also draws on the varying contexts, delivering a practical and efficient solution to edge-cloud model training.

Ranked #2 on Recommendation Systems on MovieLens 1M (Precision metric)

Feature Compression Image Classification +5

Interpretable Complex-Valued Neural Networks for Privacy Protection

1 code implementation ICLR 2020 Liyao Xiang, Haotian Ma, Hao Zhang, Yifan Zhang, Jie Ren, Quanshi Zhang

Previous studies have found that an adversary attacker can often infer unintended input information from intermediate-layer features.

Learning to Prevent Leakage: Privacy-Preserving Inference in the Mobile Cloud

no code implementations18 Dec 2019 Shuang Zhang, Liyao Xiang, CongCong Li, YiXuan Wang, Quanshi Zhang, Wei Wang, Bo Li

Powered by machine learning services in the cloud, numerous learning-driven mobile applications are gaining popularity in the market.

Neural Architecture Search Privacy Preserving +1

Deep Quaternion Features for Privacy Protection

no code implementations18 Mar 2020 Hao Zhang, Yi-Ting Chen, Liyao Xiang, Haotian Ma, Jie Shi, Quanshi Zhang

We propose a method to revise the neural network to construct the quaternion-valued neural network (QNN), in order to prevent intermediate-layer features from leaking input information.

Privacy Preserving

Rotation-Equivariant Neural Networks for Privacy Protection

no code implementations21 Jun 2020 Hao Zhang, Yiting Chen, Haotian Ma, Xu Cheng, Qihan Ren, Liyao Xiang, Jie Shi, Quanshi Zhang

Compared to the traditional neural network, the RENN uses d-ary vectors/tensors as features, in which each element is a d-ary number.

Attribute

Achieving Adversarial Robustness via Sparsity

no code implementations11 Sep 2020 Shufan Wang, Ningyi Liao, Liyao Xiang, Nanyang Ye, Quanshi Zhang

Through experiments on a variety of adversarial pruning methods, we find that weights sparsity will not hurt but improve robustness, where both weights inheritance from the lottery ticket and adversarial training improve model robustness in network pruning.

Adversarial Robustness Network Pruning

Certified Distributional Robustness via Smoothed Classifiers

no code implementations1 Jan 2021 Jungang Yang, Liyao Xiang, Ruidong Chen, Yukun Wang, Wei Wang, Xinbing Wang

We focus on certified robustness of smoothed classifiers in this work, and propose to use the worst-case population loss over noisy inputs as a robustness metric.

High-Order Relation Construction and Mining for Graph Matching

no code implementations9 Oct 2020 Hui Xu, Liyao Xiang, Youmin Le, Xiaoying Gan, Yuting Jia, Luoyi Fu, Xinbing Wang

Iterated line graphs are introduced for the first time to describe such high-order information, based on which we present a new graph matching method, called High-order Graph Matching Network (HGMN), to learn not only the local structural correspondence, but also the hyperedge relations across graphs.

Graph Matching Relation +1

Certified Distributional Robustness on Smoothed Classifiers

no code implementations21 Oct 2020 Jungang Yang, Liyao Xiang, Ruidong Chen, Yukun Wang, Wei Wang, Xinbing Wang

For smoothed classifiers, we propose the worst-case adversarial loss over input distributions as a robustness certificate.

Improved Matrix Gaussian Mechanism for Differential Privacy

no code implementations30 Apr 2021 Jungang Yang, Liyao Xiang, Weiting Li, Wei Liu, Xinbing Wang

The wide deployment of machine learning in recent years gives rise to a great demand for large-scale and high-dimensional data, for which the privacy raises serious concern.

Privacy Threats Analysis to Secure Federated Learning

no code implementations24 Jun 2021 Yuchen Li, Yifan Bao, Liyao Xiang, Junhan Liu, Cen Chen, Li Wang, Xinbing Wang

Federated learning is emerging as a machine learning technique that trains a model across multiple decentralized parties.

BIG-bench Machine Learning Federated Learning +1

Context-aware deep model compression for edge cloud computing

no code implementations International Conference on Distributed Computing Systems 2020 Lingdong Wang, Liyao Xiang, Jiayu Xu, Jiaju Chen, Xing Zhao, Dixi Yao, Xinbing Wang, Baochun Li

While deep neural networks (DNNs) have led to a paradigm shift, its exorbitant computational requirement has always been a roadblock in its deployment to the edge, such as wearable devices and smartphones.

Cloud Computing Image Classification +1

Crossword: A Semantic Approach to Data Compression via Masking

no code implementations3 Apr 2023 Mingxiao Li, Rui Jin, Liyao Xiang, Kaiming Shen, Shuguang Cui

The traditional methods for data compression are typically based on the symbol-level statistics, with the information source modeled as a long sequence of i. i. d.

Data Compression

Crafter: Facial Feature Crafting against Inversion-based Identity Theft on Deep Models

no code implementations14 Jan 2024 Shiming Wang, Zhe Ji, Liyao Xiang, Hao Zhang, Xinbing Wang, Chenghu Zhou, Bo Li

However, such methods can not defend against adaptive attacks, in which an attacker takes a countermove against a known defence strategy.

AceMap: Knowledge Discovery through Academic Graph

no code implementations5 Mar 2024 Xinbing Wang, Luoyi Fu, Xiaoying Gan, Ying Wen, Guanjie Zheng, Jiaxin Ding, Liyao Xiang, Nanyang Ye, Meng Jin, Shiyu Liang, Bin Lu, Haiwen Wang, Yi Xu, Cheng Deng, Shao Zhang, Huquan Kang, Xingli Wang, Qi Li, Zhixin Guo, Jiexing Qi, Pan Liu, Yuyang Ren, Lyuwen Wu, Jungang Yang, Jianping Zhou, Chenghu Zhou

While existing scientific search engines excel at delivering search results based on relational databases, they often neglect the analysis of collaborations between scientific entities and the evolution of ideas, as well as the in-depth analysis of content within scientific publications.

Knowledge Graphs Reading Comprehension

Hufu: A Modality-Agnositc Watermarking System for Pre-Trained Transformers via Permutation Equivariance

no code implementations9 Mar 2024 Hengyuan Xu, Liyao Xiang, Xingjun Ma, Borui Yang, Baochun Li

The permutation equivariance ensures minimal interference between these two sets of model weights and thus high fidelity on downstream tasks.

Entity Alignment with Unlabeled Dangling Cases

no code implementations16 Mar 2024 Hang Yin, Dong Ding, Liyao Xiang, Yuheng He, Yihan Wu, Xinbing Wang, Chenghu Zhou

We investigate the entity alignment problem with unlabeled dangling cases, meaning that there are entities in the source or target graph having no counterparts in the other, and those entities remain unlabeled.

Entity Alignment Representation Learning

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