Search Results for author: Youcheng Sun

Found 24 papers, 14 papers with code

Proactive Load-Shaping Strategies with Privacy-Cost Trade-offs in Residential Households based on Deep Reinforcement Learning

no code implementations29 May 2024 Ruichang Zhang, Youcheng Sun, Mustafa A. Mustafa

Smart meters play a crucial role in enhancing energy management and efficiency, but they raise significant privacy concerns by potentially revealing detailed user behaviors through energy consumption patterns.

energy management Management +1

Does Knowledge Graph Really Matter for Recommender Systems?

1 code implementation4 Apr 2024 Haonan Zhang, Dongxia Wang, Zhu Sun, Yanhui Li, Youcheng Sun, HuiZhi Liang, Wenhai Wang

We consider the scenarios where knowledge in a KG gets completely removed, randomly distorted and decreased, and also where recommendations are for cold-start users.

Knowledge Graphs Recommendation Systems

QNNRepair: Quantized Neural Network Repair

no code implementations23 Jun 2023 Xidan Song, Youcheng Sun, Mustafa A. Mustafa, Lucas C. Cordeiro

It accepts the full-precision and weight-quantized neural networks and a repair dataset of passing and failing tests.

Data Free Quantization Fault localization

AIREPAIR: A Repair Platform for Neural Networks

1 code implementation24 Nov 2022 Xidan Song, Youcheng Sun, Mustafa A. Mustafa, Lucas Cordeiro

We present AIREPAIR, a platform for repairing neural networks.

Safety Analysis of Autonomous Driving Systems Based on Model Learning

no code implementations23 Nov 2022 Renjue Li, Tianhang Qin, Pengfei Yang, Cheng-Chao Huang, Youcheng Sun, Lijun Zhang

The safety properties proved in the resulting surrogate model apply to the original ADS with a probabilistic guarantee.

Autonomous Driving

An Overview of Structural Coverage Metrics for Testing Neural Networks

1 code implementation5 Aug 2022 Muhammad Usman, Youcheng Sun, Divya Gopinath, Rishi Dange, Luca Manolache, Corina S. Pasareanu

Deep neural network (DNN) models, including those used in safety-critical domains, need to be thoroughly tested to ensure that they can reliably perform well in different scenarios.

DNN Testing

VeriFi: Towards Verifiable Federated Unlearning

no code implementations25 May 2022 Xiangshan Gao, Xingjun Ma, Jingyi Wang, Youcheng Sun, Bo Li, Shouling Ji, Peng Cheng, Jiming Chen

One desirable property for FL is the implementation of the right to be forgotten (RTBF), i. e., a leaving participant has the right to request to delete its private data from the global model.

Federated Learning

VPN: Verification of Poisoning in Neural Networks

no code implementations8 May 2022 Youcheng Sun, Muhammad Usman, Divya Gopinath, Corina S. Păsăreanu

Neural networks are successfully used in a variety of applications, many of them having safety and security concerns.

Data Poisoning Image Classification +1

AntidoteRT: Run-time Detection and Correction of Poison Attacks on Neural Networks

1 code implementation31 Jan 2022 Muhammad Usman, Youcheng Sun, Divya Gopinath, Corina S. Pasareanu

For correction, we propose an input correction technique that uses a differential analysis to identify the trigger in the detected poisoned images, which is then reset to a neutral color.

Image Classification

NNrepair: Constraint-based Repair of Neural Network Classifiers

1 code implementation23 Mar 2021 Muhammad Usman, Divya Gopinath, Youcheng Sun, Yannic Noller, Corina Pasareanu

We present novel strategies to enable precise yet efficient repair such as inferring correctness specifications to act as oracles for intermediate layer repair, and generation of experts for each class.

Fault localization

Explanations for Occluded Images

no code implementations ICCV 2021 Hana Chockler, Daniel Kroening, Youcheng Sun

Existing algorithms for explaining the output of image classifiers perform poorly on inputs where the object of interest is partially occluded.

RobOT: Robustness-Oriented Testing for Deep Learning Systems

1 code implementation11 Feb 2021 Jingyi Wang, Jialuo Chen, Youcheng Sun, Xingjun Ma, Dongxia Wang, Jun Sun, Peng Cheng

A key part of RobOT is a quantitative measurement on 1) the value of each test case in improving model robustness (often via retraining), and 2) the convergence quality of the model robustness improvement.

Software Engineering

Towards Practical Robustness Analysis for DNNs based on PAC-Model Learning

1 code implementation25 Jan 2021 Renjue Li, Pengfei Yang, Cheng-Chao Huang, Youcheng Sun, Bai Xue, Lijun Zhang

It is shown that DeepPAC outperforms the state-of-the-art statistical method PROVERO, and it achieves more practical robustness analysis than the formal verification tool ERAN.

Adversarial Attack DNN Testing

Ranking Policy Decisions

2 code implementations NeurIPS 2021 Hadrien Pouget, Hana Chockler, Youcheng Sun, Daniel Kroening

Policies trained via Reinforcement Learning (RL) are often needlessly complex, making them difficult to analyse and interpret.

Atari Games Reinforcement Learning (RL)

Reliability Validation of Learning Enabled Vehicle Tracking

no code implementations6 Feb 2020 Youcheng Sun, Yifan Zhou, Simon Maskell, James Sharp, Xiaowei Huang

However, it is unclear if and how the adversarial examples over learning components can affect the overall system-level reliability.

Coverage Guided Testing for Recurrent Neural Networks

1 code implementation5 Nov 2019 Wei Huang, Youcheng Sun, Xingyu Zhao, James Sharp, Wenjie Ruan, Jie Meng, Xiaowei Huang

The test metrics and test case generation algorithm are implemented into a tool TestRNN, which is then evaluated on a set of LSTM benchmarks.

Defect Detection Drug Discovery +3

Explaining Image Classifiers using Statistical Fault Localization

1 code implementation6 Aug 2019 Youcheng Sun, Hana Chockler, Xiaowei Huang, Daniel Kroening

The black-box nature of deep neural networks (DNNs) makes it impossible to understand why a particular output is produced, creating demand for "Explainable AI".

Fault localization

testRNN: Coverage-guided Testing on Recurrent Neural Networks

1 code implementation20 Jun 2019 Wei Huang, Youcheng Sun, Xiaowei Huang, James Sharp

Recurrent neural networks (RNNs) have been widely applied to various sequential tasks such as text processing, video recognition, and molecular property prediction.

Molecular Property Prediction Property Prediction +1

Concolic Testing for Deep Neural Networks

2 code implementations30 Apr 2018 Youcheng Sun, Min Wu, Wenjie Ruan, Xiaowei Huang, Marta Kwiatkowska, Daniel Kroening

Concolic testing combines program execution and symbolic analysis to explore the execution paths of a software program.

Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the $L_0$ Norm

2 code implementations16 Apr 2018 Wenjie Ruan, Min Wu, Youcheng Sun, Xiaowei Huang, Daniel Kroening, Marta Kwiatkowska

In this paper we focus on the $L_0$ norm and aim to compute, for a trained DNN and an input, the maximal radius of a safe norm ball around the input within which there are no adversarial examples.

Testing Deep Neural Networks

no code implementations10 Mar 2018 Youcheng Sun, Xiaowei Huang, Daniel Kroening, James Sharp, Matthew Hill, Rob Ashmore

In this paper, inspired by the MC/DC coverage criterion, we propose a family of four novel test criteria that are tailored to structural features of DNNs and their semantics.

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