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The next generation of AI systems requires strong safety guarantees.
Information capacity of the hopfield model
Y. Abu-Mostafa and J. St. Jacques · 1985
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Model checking of real-time reachability properties using abstractions
C. Daws and S. Tripakis · 1998
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Neural-based dynamic modeling of nonlinear microwave circuits
J. Xu, M. Yagoub, R. Ding, and Q.-J. Zhang · 2002
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Neural code completion
C. Liu, X. Wang, R. Shin, J. E. Gonzalez, and D. Song · 2016
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Simple black-box adversarial attacks on deep neural networks
N. Narodytska and S. Kasiviswanathan · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
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Mull it over: Mutation testing based on llvm
A. Denisov and S. Pankevich · 2018
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Bipartite expander hopfield networks as self-decoding high-capacity error correcting codes
R. Chaudhuri and I. Fiete · 2019
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Trends and challenges in the vulnerability mitigation landscape
M. Miller · 2019
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TensorFuzz: Debugging neural networks with coverage-guided fuzzing
A. Odena, C. Olsson, D. Andersen, and I. Goodfellow · 2019
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Language models are unsupervised multitask learners
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever · 2019
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Pitfalls in machine learning research: Reexamining the development cycle
S. Biderman and W. J. Scheirer · 2020
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Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al · 2020
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A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability
X. Huang, D. Kroening, W. Ruan, J. Sharp, Y. Sun, E. Thamo, M. Wu, and X. Yi · 2020
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Taxonomy of real faults in deep learning systems
N. Humbatova, G. Jahangirova, G. Bavota, V. Riccio, A. Stocco, and P. Tonella · 2020
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Keras2c: A library for converting keras neural networks to real-time compatible c
R. Conlin, K. Erickson, J. Abbate, and E. Kolemen · 2021
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Exploring the limits of out-of-distribution detection
S. Fort, J. Ren, and B. Lakshminarayanan · 2021
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Audee: Automated testing for deep learning frameworks
Q. Guo, X. Xie, Y. Li, X. Zhang, Y. Liu, X. Li, and C. Shen · 2021
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A framework for understanding sources of harm throughout the machine learning life cycle
2023 cwe top 25 most dangerous software weaknesses, 2023
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Open neural network exchange: The open standard for machine learning interoperability, 2023
O. Community · 2023
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keras2c github repository, 2023
R. Conlin · 2023
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Differential testing of cross deep learning framework APIs: Revealing inconsistencies and vulnerabilities
Z. Deng, G. Meng, K. Chen, T. Liu, L. Xiang, and C. Chen · 2023
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Automated repair of programs from large language models
Z. Fan, X. Gao, M. Mirchev, A. Roychoudhury, and S. H. Tan · 2023
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NeuroCodeBench: a Plain C Neural Network Benchmark for Software Verification
E. Manino, R. S. Menezes, F. Shmarov, and L. C. Cordeiro · 2023
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Learning density distribution of reachable states for autonomous systems
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Safe reinforcement learning benchmark environments for aerospace control systems
U. J. Ravaioli, J. Cunningham, J. McCarroll, V. Gangal, K. Dunlap, and K. L. Hobbs · 2022
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Pytorch, 2023
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The Fourth International Verification of Neural Networks Competition (VNN-COMP 2023): Summary and Results, 2023
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Deep learning-based wave digital modeling of rate-dependent hysteretic nonlinearities for virtual analog applications
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Bugs in machine learning-based systems: a faultload benchmark
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The third international verification of neural networks competition (vnn-comp 2022): Summary and results, 2023
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onnx2c github repository, 2023
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A prompt pattern catalog to enhance prompt engineering with chatgpt
J. White, Q. Fu, S. Hays, M. Sandborn, C. Olea, H. Gilbert, A. Elnashar, J. Spencer-Smith, and D. C. Schmidt · 2023
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https://platform.openai.com/docs/guides/prompt-engineering/tactic-use-code-execution-to-perform-more-accurate-calculations-or-call-external-apis
Prompt engineering · 2024
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SMT-Based Bounded Model Checking for Embedded ANSI-C Software, July 2009
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