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Florian Tambon
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2020 – today
- 2024
- [j5]Florian Tambon, Amin Nikanjam, Le An, Foutse Khomh, Giuliano Antoniol:
Silent bugs in deep learning frameworks: an empirical study of Keras and TensorFlow. Empir. Softw. Eng. 29(1): 10 (2024) - [j4]Mohammad Mehdi Morovati, Amin Nikanjam, Florian Tambon, Foutse Khomh, Zhen Ming (Jack) Jiang:
Bug characterization in machine learning-based systems. Empir. Softw. Eng. 29(1): 14 (2024) - [j3]Mohammad Mehdi Morovati, Florian Tambon, Mina Taraghi, Amin Nikanjam, Foutse Khomh:
Common challenges of deep reinforcement learning applications development: an empirical study. Empir. Softw. Eng. 29(4): 95 (2024) - [c3]Sylvain Kouemo Ngassom, Arghavan Moradi Dakhel, Florian Tambon, Foutse Khomh:
Chain of Targeted Verification Questions to Improve the Reliability of Code Generated by LLMs. AIware 2024 - [c2]Mina Taraghi, Gianolli Dorcelus, Armstrong Foundjem, Florian Tambon, Foutse Khomh:
Deep Learning Model Reuse in the HuggingFace Community: Challenges, Benefit and Trends. SANER 2024: 512-523 - [i12]Mina Taraghi, Gianolli Dorcelus, Armstrong Foundjem, Florian Tambon, Foutse Khomh:
Deep Learning Model Reuse in the HuggingFace Community: Challenges, Benefit and Trends. CoRR abs/2401.13177 (2024) - [i11]Florian Tambon, Arghavan Moradi Dakhel, Amin Nikanjam, Foutse Khomh, Michel C. Desmarais, Giuliano Antoniol:
Bugs in Large Language Models Generated Code: An Empirical Study. CoRR abs/2403.08937 (2024) - [i10]Sylvain Kouemo Ngassom, Arghavan Moradi Dakhel, Florian Tambon, Foutse Khomh:
Chain of Targeted Verification Questions to Improve the Reliability of Code Generated by LLMs. CoRR abs/2405.13932 (2024) - [i9]Florian Tambon, Amin Nikanjam, Foutse Khomh, Giuliano Antoniol:
Assessing Programming Task Difficulty for Efficient Evaluation of Large Language Models. CoRR abs/2407.21227 (2024) - 2023
- [j2]Florian Tambon, Foutse Khomh, Giuliano Antoniol:
A probabilistic framework for mutation testing in deep neural networks. Inf. Softw. Technol. 155: 107129 (2023) - [c1]Florian Tambon, Vahid Majdinasab, Amin Nikanjam, Foutse Khomh, Giuliano Antoniol:
Mutation Testing of Deep Reinforcement Learning Based on Real Faults. ICST 2023: 188-198 - [i8]Florian Tambon, Vahid Majdinasab, Amin Nikanjam, Foutse Khomh, Giuliano Antoniol:
Mutation Testing of Deep Reinforcement Learning Based on Real Faults. CoRR abs/2301.05651 (2023) - [i7]Mohammad Mehdi Morovati, Amin Nikanjam, Florian Tambon, Foutse Khomh, Zhen Ming Jiang:
Bug Characterization in Machine Learning-based Systems. CoRR abs/2307.14512 (2023) - [i6]Mohammad Mehdi Morovati, Florian Tambon, Mina Taraghi, Amin Nikanjam, Foutse Khomh:
Common Challenges of Deep Reinforcement Learning Applications Development: An Empirical Study. CoRR abs/2310.09575 (2023) - [i5]Florian Tambon, Foutse Khomh, Giuliano Antoniol:
GIST: Generated Inputs Sets Transferability in Deep Learning. CoRR abs/2311.00801 (2023) - 2022
- [j1]Florian Tambon, Gabriel Laberge, Le An, Amin Nikanjam, Paulina Stevia Nouwou Mindom, Yann Pequignot, Foutse Khomh, Giulio Antoniol, Ettore Merlo, François Laviolette:
How to certify machine learning based safety-critical systems? A systematic literature review. Autom. Softw. Eng. 29(2): 38 (2022) - [i4]Florian Tambon, Foutse Khomh, Giuliano Antoniol:
A Probabilistic Framework for Mutation Testing in Deep Neural Networks. CoRR abs/2208.06018 (2022) - 2021
- [i3]Florian Tambon, Giulio Antoniol, Foutse Khomh:
HOMRS: High Order Metamorphic Relations Selector for Deep Neural Networks. CoRR abs/2107.04863 (2021) - [i2]Florian Tambon, Gabriel Laberge, Le An, Amin Nikanjam, Paulina Stevia Nouwou Mindom, Yann Pequignot, Foutse Khomh, Giulio Antoniol, Ettore Merlo, François Laviolette:
How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review. CoRR abs/2107.12045 (2021) - [i1]Florian Tambon, Amin Nikanjam, Le An, Foutse Khomh, Giuliano Antoniol:
Silent Bugs in Deep Learning Frameworks: An Empirical Study of Keras and TensorFlow. CoRR abs/2112.13314 (2021)
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