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## Abstract 本文：DeepCT Task: Testing DL Models with Combinatorial Testing Method: 1. 将输出值的空间离散化为区间，以便覆盖每个区间,对不同层内的神经元交互进⾏采样，并减少必须执⾏的测试输⼊的数量。 2. a set o 阅读全文
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## Abstract 本文：描述automatic differentiation module of PyTorch 包括：Lua Torch, Chainer, HIPS Autograd Task: Provides a high-performance environment on dif 阅读全文
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## Abstract Github: https://github.com/shijy16/ACETest 背景： 1. DL operators 用来计算多维tensors，很重要 本文：ACETest Task: automatically extract input validation c 阅读全文
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## Abstract Github: https://github.com/marsggbo/automl_a_survey_of_state_of_the_art 本文： 1. intro AutoML methods: data preparation, feature engineering 阅读全文
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## Abstract 背景： 1. the de facto standard to assess the quality of DNNs in the industry is to check their performance (accuracy) on a collected set of 阅读全文
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## Abstract ## 1. Intro ## 2. Background ### 2.1 Program Understanding and Generation Tasks ### 2.2 NL-PL Pre-Trained Models ![](https://img2023.cnblo 阅读全文
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## Abstract Task: an artifact to accompany Reducing DNN Properties to Enable Falsification with Adversarial Attacks 包括DNNF ⼯具、数据和脚本 a VirtualBox virtu 阅读全文
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## Abstract 本文：CRADLE Task: finding and localizing bugs in DL Libraries Method: 1. differential testing between DL Libraries 2. 使用异常传播跟踪和分析来定位DL库中的错误函 阅读全文
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## Abstract 本文：Audee Github: https://github.com/jikechao/Audee Task: test DL frameworks and localizing bugs Method: 1. search-based 2. 3 different mut 阅读全文
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## Abstract 本文: LEMON Task: Fuzzing DL Libraries Method: 1. specified mutators for DL Libraries to explore different invoking sequences of library cod 阅读全文