Search Results for author: Yufei Chen

Found 13 papers, 4 papers with code

Worst-Case Dynamic Power Distribution Network Noise Prediction Using Convolutional Neural Network

no code implementations27 Apr 2022 Xiao Dong, Yufei Chen, Xunzhao Yin, Cheng Zhuo

Worst-case dynamic PDN noise analysis is an essential step in PDN sign-off to ensure the performance and reliability of chips.

FairNeuron: Improving Deep Neural Network Fairness with Adversary Games on Selective Neurons

1 code implementation6 Apr 2022 Xuanqi Gao, Juan Zhai, Shiqing Ma, Chao Shen, Yufei Chen, Qian Wang

To solve this issue, there has been a number of work trying to improve model fairness by using an adversarial game in model level.


Property Inference Attacks Against GANs

1 code implementation15 Nov 2021 Junhao Zhou, Yufei Chen, Chao Shen, Yang Zhang

In addition, we show that our attacks can be used to enhance the performance of membership inference against GANs.

Fairness Inference Attack

Teacher Model Fingerprinting Attacks Against Transfer Learning

no code implementations23 Jun 2021 Yufei Chen, Chao Shen, Cong Wang, Yang Zhang

To this end, we propose a teacher model fingerprinting attack to infer the origin of a student model, i. e., the teacher model it transfers from.

Transfer Learning

Parsing into Variable-in-situ Logico-Semantic Graphs

no code implementations ACL 2020 Yufei Chen, Weiwei Sun

We propose variable-in-situ logico-semantic graphs to bridge the gap between semantic graph and logical form parsing.

A Unified Framework for Analyzing and Detecting Malicious Examples of DNN Models

1 code implementation26 Jun 2020 Kaidi Jin, Tianwei Zhang, Chao Shen, Yufei Chen, Ming Fan, Chenhao Lin, Ting Liu

In this paper, we present a unified framework for detecting malicious examples and protecting the inference results of Deep Learning models.

Adversarial Defense

Peking at MRP 2019: Factorization- and Composition-Based Parsing for Elementary Dependency Structures

no code implementations CONLL 2019 Yufei Chen, Yajie Ye, Weiwei Sun

We design, implement and evaluate two semantic parsers, which represent factorization- and composition-based approaches respectively, for Elementary Dependency Structures (EDS) at the CoNLL 2019 Shared Task on Cross-Framework Meaning Representation Parsing.

Seeing is Not Believing: Camouflage Attacks on Image Scaling Algorithms

no code implementations USENIX Security Symposium 2019 Qixue Xiao, Yufei Chen, Chao Shen, Yu Chen, Kang Li

We also present an algorithm that can successfully enable attacks against famous cloud-based image services (such as those from Microsoft Azure, Aliyun, Baidu, and Tencent) and cause obvious misclassification effects, even when the details of image processing (such as the exact scaling algorithm and scale dimension parameters) are hidden in the cloud.

Data Poisoning Image Classification

Parsing Chinese Sentences with Grammatical Relations

no code implementations CL 2019 Weiwei Sun, Yufei Chen, Xiaojun Wan, Meichun Liu

In this work, we propose to represent grammatical information using general directed dependency graphs.

Pre- and In-Parsing Models for Neural Empty Category Detection

no code implementations ACL 2018 Yufei Chen, Yuan-Yuan Zhao, Weiwei Sun, Xiaojun Wan

Motivated by the positive impact of empty category on syntactic parsing, we study neural models for pre- and in-parsing detection of empty category, which has not previously been investigated.

Dependency Parsing Structured Prediction

Accurate SHRG-Based Semantic Parsing

no code implementations ACL 2018 Yufei Chen, Weiwei Sun, Xiaojun Wan

We demonstrate that an SHRG-based parser can produce semantic graphs much more accurately than previously shown, by relating synchronous production rules to the syntacto-semantic composition process.

Semantic Composition Semantic Parsing

Context-Aware Gaussian Fields for Non-Rigid Point Set Registration

no code implementations CVPR 2016 Gang Wang, Zhicheng Wang, Yufei Chen, Qiangqiang Zhou, Weidong Zhao

Point set registration (PSR) is a fundamental problem in computer vision and pattern recognition, and it has been successfully applied to many applications.

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