Nicholas Synovic

Orcid: 0000-0003-0413-4594

According to our database1, Nicholas Synovic authored at least 11 papers between 2022 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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PhD thesis 
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Bibliography

2024
Challenges and practices of deep learning model reengineering: A case study on computer vision.
Empir. Softw. Eng., November, 2024

PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software.
Proceedings of the 21st IEEE/ACM International Conference on Mining Software Repositories, 2024

What do we know about Hugging Face? A systematic literature review and quantitative validation of qualitative claims.
Proceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, 2024

An automated approach for improving the inference latency and energy efficiency of pretrained CNNs by removing irrelevant pixels with focused convolutions.
Proceedings of the 29th Asia and South Pacific Design Automation Conference, 2024

2023
An automated approach for improving the inference latency and energy efficiency of pretrained CNNs by removing irrelevant pixels with focused convolutions.
CoRR, 2023

PeaTMOSS: Mining Pre-Trained Models in Open-Source Software.
CoRR, 2023

PTMTorrent: A Dataset for Mining Open-source Pre-trained Model Packages.
Proceedings of the 20th IEEE/ACM International Conference on Mining Software Repositories, 2023

Reusing Deep Learning Models: Challenges and Directions in Software Engineering.
Proceedings of the IEEE John Vincent Atanasoff International Symposium on Modern Computing, 2023

An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry.
Proceedings of the 45th IEEE/ACM International Conference on Software Engineering, 2023

2022
An Empirical Study of Artifacts and Security Risks in the Pre-trained Model Supply Chain.
Proceedings of the 2022 ACM Workshop on Software Supply Chain Offensive Research and Ecosystem Defenses, 2022

Snapshot Metrics Are Not Enough: Analyzing Software Repositories with Longitudinal Metrics.
Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, 2022


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