Výsledky vyhľadávania - CCS Concepts Computing methodologies → Model verification and validation

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  1. 1

    VISLIX: An XAI Framework for Validating Vision Models with Slice Discovery and Analysis Autor Yan, Xinyuan, Xuan, Xiwei, Ono, Jorge Piazentin, Guo, Jiajing, Mohanty, Vikram, Kumar, Shekar Arvind, Gou, Liang, Wang, Bei, Ren, Liu

    ISSN: 0167-7055, 1467-8659
    Vydavateľské údaje: Oxford Blackwell Publishing Ltd 01.06.2025
    Vydané v Computer graphics forum (01.06.2025)
    “… Despite its popularity and effectiveness, data slicing for vision model validation faces several challenges…”
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    Journal Article
  2. 2

    Interactively Assessing Disentanglement in GANs Autor Jeong, Sangwon, Liu, Shusen, Berger, Matthew

    ISSN: 0167-7055, 1467-8659
    Vydavateľské údaje: Oxford Blackwell Publishing Ltd 01.06.2022
    Vydané v Computer graphics forum (01.06.2022)
    “…Generative adversarial networks (GAN) have witnessed tremendous growth in recent years, demonstrating wide applicability in many domains. However, GANs remain…”
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    Journal Article
  3. 3

    Character navigation in dynamic environments based on optical flow Autor López, Axel, Chaumette, François, Marchand, Eric, Pettré, Julien

    ISSN: 0167-7055, 1467-8659
    Vydavateľské údaje: Oxford Blackwell Publishing Ltd 01.05.2019
    Vydané v Computer graphics forum (01.05.2019)
    “… In this work, we propose a synthetic vision model that uses visual features to steer agents through dynamic environments…”
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    Journal Article
  4. 4

    REFINE: realistic fault injection via compiler-based instrumentation for accuracy, portability and speed Autor Georgakoudis, Giorgis, Laguna, Ignacio, Nikolopoulos, Dimitrios S., Schulz, Martin

    ISBN: 9781450351140, 145035114X
    ISSN: 2167-4337
    Vydavateľské údaje: New York, NY, USA ACM 12.11.2017
    “…Compiler-based fault injection (FI) has become a popular technique for resilience studies to understand the impact of soft errors in supercomputing systems…”
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  5. 5

    Generating Accurate Assert Statements for Unit Test Cases using Pretrained Transformers Autor Tufano, Michele, Drain, Dawn, Svyatkovskiy, Alexey, Sundaresan, Neel

    Vydavateľské údaje: ACM 01.05.2022
    “… Our approach is based on a state-of-the-art transformer model initially pretrained on an English textual corpus…”
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  6. 6

    Manifold for Machine Learning Assurance Autor Byun, Taejoon, Rayadurgam, Sanjai

    Vydavateľské údaje: ACM 01.10.2020
    “…The increasing use of machine-learning (ML) enabled systems in critical tasks fuels the quest for novel verification and validation techniques yet grounded in accepted system assurance principles…”
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  7. 7

    Compensation analysis of subsidence angle of supersonic low-altitude cruise target projectile launch Autor Guo, Hongli, Ren, Shaofeng, Yan, Huaiqiang, Zhao, Hongtao, Wei, Chunshuang, Gao, Yun

    ISSN: 1742-6588, 1742-6596
    Vydavateľské údaje: Bristol IOP Publishing 01.08.2022
    Vydané v Journal of physics. Conference series (01.08.2022)
    “…Aiming at the launching process of the supersonic barrel-type spinning target projectile, and improving the control accuracy of the open-loop controlled target…”
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    Journal Article
  8. 8

    DataRaceBench: a benchmark suite for systematic evaluation of data race detection tools Autor Liao, Chunhua, Lin, Pei-Hung, Asplund, Joshua, Schordan, Markus, Karlin, Ian

    ISBN: 9781450351140, 145035114X
    ISSN: 2167-4337
    Vydavateľské údaje: New York, NY, USA ACM 12.11.2017
    “… We focus on data race detection in programs written in OpenMP, the popular parallel programming model for multi-threaded applications…”
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  9. 9

    A Method and Experiment to evaluate Deep Neural Networks as Test Oracles for Scientific Software Autor de Santiago Junior, Valdivino Alexandre

    Vydavateľské údaje: ACM 01.05.2022
    “…) with up to 161layers, playing the role of test oracle procedures for testing scientific models…”
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  10. 10

    Towards Benchmarking the Coverage of Automated Testing Tools in Android against Manual Testing Autor Thung, Ferdian, Irsan, Ivana Clairine, Liu, Jiakun, Lo, David

    Vydavateľské údaje: ACM 14.04.2024
    “… A lot of testing methodologies are employed such as model-based testing, search-based testing, random testing, fuzzing, concolic execution, and mutation…”
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