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Creators/Authors contains: "Pouchet, Louis-Noel"

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  1. ; ; ; (, 2022 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS))
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  2. ; ; ; ; ; ; (, ACM International Symposium on Performance Analysis of Systems and Software (ISPASS))
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  3. ; ; ; ; ; ; (, 2022 IEEE/ACM International Symposium on Code Generation and Optimization (CGO))
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  4. ; ; ; ; ; (, EEE International Conference on Cluster Computing (CLUSTER))
    null (Ed.)
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  5. ; ; ; ; (, IEEE Access)
  6. ; (, 2019 International Joint Conference on Neural Networks (IJCNN))
    One of the challenges of using machine learning techniques with medical data is the frequent dearth of source image data on which to train. A representative example is automated lung cancer diagnosis, where nodule images need to be classified as suspicious or benign. In this work we propose an automatic synthetic lung nodule image generator. Our 3D shape generator is designed to augment the variety of 3D images. Our proposed system takes root in autoencoder techniques, and we provide extensive experimental characterization that demonstrates its ability to produce quality synthetic images. 
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  7. ; ; ; ; ; (, IEEE Transactions on Software Engineering)
    This paper presents a novel end-to-end approach to program repair based on sequence-to-sequence learning. We devise, implement, and evaluate a technique, called SEQUENCER, for fixing bugs based on sequence-to-sequence learning on source code. This approach uses the copy mechanism to overcome the unlimited vocabulary problem that occurs with big code. Our system is data-driven; we train it on 35,578 samples, carefully curated from commits to open-source repositories. We evaluate SEQUENCER on 4,711 independent real bug fixes, as well on the Defects4J benchmark used in program repair research. SEQUENCER is able to perfectly predict the fixed line for 950/4,711 testing samples, and find correct patches for 14 bugs in Defects4J benchmark. SEQUENCER captures a wide range of repair operators without any domain-specific top-down design. 
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  8. ; ; ; ; ; ; ; (, 2019 IEEE/ACM International Symposium on Code Generation and Optimization (CGO))
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