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Performance analysis of out-of-distribution detection on trained neural networks
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Towards a Framework to Manage Perceptual Uncertainty for Safe Automated Driving
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Experimentation in Software Engineering
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ImageNet Large Scale Visual Recognition Challenge
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Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
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Deep Residual Learning for Image Recognition
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Densely Connected Convolutional Networks
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Guiding Deep Learning System Testing Using Surprise Adequacy
inferredcites →https://doi.org/10.1109/icse40121.2019openalex-snapshot
2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)
inferredcites →https://doi.org/10.1109/iv43497.2018openalex-snapshot
2018 IEEE Intelligent Vehicles Symposium (IV)
inferredcites →https://doi.org/10.1109/ivs.2018.8500450openalex-snapshot
Improved Robustness and Safety for Autonomous Vehicle Control with Adversarial Reinforcement Learning
inferredcites →https://doi.org/10.1109/seaa.2019.00026openalex-snapshot
Performance Analysis of Out-of-Distribution Detection on Various Trained Neural Networks
inferredcites →https://doi.org/10.1109/seaa51224.2020openalex-snapshot
2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)
inferredcites →https://doi.org/10.1109/sp.2016.41openalex-snapshot
Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks
inferredcites →https://doi.org/10.1109/sp.2017.49openalex-snapshot
Towards Evaluating the Robustness of Neural Networks
inferredcites →https://doi.org/10.1109/sp36276.2017openalex-snapshot
2017 IEEE Symposium on Security and Privacy (SP)
inferredcites →https://doi.org/10.1109/sp37862.2018openalex-snapshot
2018 IEEE Symposium on Security and Privacy (SP)
inferredcites →https://doi.org/10.1145/3132747openalex-snapshot
Proceedings of the 26th Symposium on Operating Systems Principles
inferredcites →https://doi.org/10.1145/3194085openalex-snapshot
Proceedings of the 1st International Workshop on Software Engineering for AI in Autonomous Systems
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DLFuzz: differential fuzzing testing of deep learning systems
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Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering
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Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
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Safely Entering the Deep: A Review of Verification and Validation for Machine Learning and a Challenge Elicitation in the Automotive Industry
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Towards Structured Evaluation of Deep Neural Network Supervisors
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Learning Multiple Layers of Features from Tiny Images
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Experimentation in software engineering
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Efficient Token Sparsification Through the Lens of Infused Knowledge
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