most citedAn Experience Report on Regression-Free Repair of Deep Neural Network Model

3 citations · 5 across the 5 of their papers we have counts for

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5 papers

cs.SE2025

Development of Automated Software Design Document Review Methods Using Large Language Models

Takasaburo Fukuda, Takao Nakagawa, Keisuke Miyazaki +1

In this study, we explored an approach to automate the review process of software design documents by using LLM. We first analyzed the review methods of design documents and organi…

cs.SE2025★ 3 cited

An Experience Report on Regression-Free Repair of Deep Neural Network Model

Takao Nakagawa, Susumu Tokumoto, Shogo Tokui +1

Systems based on Deep Neural Networks (DNNs) are increasingly being used in industry. In the process of system operation, DNNs need to be updated in order to improve their performa…

cs.LG2022★ 2 cited

An Exploratory Study of AI System Risk Assessment from the Lens of Data Distribution and Uncertainty

Zhijie Wang, Yuheng Huang, Lei Ma +3

Deep learning (DL) has become a driving force and has been widely adopted in many domains and applications with competitive performance. In practice, to solve the nontrivial and co…

cs.LG2022

Practical Insights of Repairing Model Problems on Image Classification

Akihito Yoshii, Susumu Tokumoto, Fuyuki Ishikawa

Additional training of a deep learning model can cause negative effects on the results, turning an initially positive sample into a negative one (degradation). Such degradation is…

cs.LG2022

NeuRecover: Regression-Controlled Repair of Deep Neural Networks with Training History

Shogo Tokui, Susumu Tokumoto, Akihito Yoshii +4

Systematic techniques to improve quality of deep neural networks (DNNs) are critical given the increasing demand for practical applications including safety-critical ones. The key…