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[Gentle Introduction, Easy read] Everything You Always Wanted To Know About Mathematics https://www.math.cmu.edu/~jmackey/151_128/bws_book.pdf
A Guided Journey Into the World of Abstract Mathematics and the Writing of Proofs
- SIMILARITY OF NEURAL NETWORK MODELS: A SURVEY OF FUNCTIONAL AND REPRESENTATIONAL MEASURES https://arxiv.org/pdf/2305.06329.pdf
metrics to check a) how much hidden layers differ (representational similarity), and
Target: Journal https://www.sciencedirect.com/science/article/pii/S1569190X23000175
- [Journal, cosimulation] Optimizing vehicle dynamics co-simulation performance by introducing mesoscopic traffic simulation https://www.sciencedirect.com/science/article/pii/S1569190X23000175
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An algorithmic perspective on Imitation Learning https://arxiv.org/pdf/1811.06711.pdf Book, very useful
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Tutorials/Implementation of IL algorithms https://imitation.readthedocs.io/en/latest/algorithms/bc.html
- Principles of model checking https://is.ifmo.ru/books/_principles_of_model_checking.pdf Book: automata, LTL, equivalence, etc.
- Towards an Ontology for Scenario Definition for the Assessment of Automated Vehicles: An Object-Oriented Framework, 2021 arxiv, IEEE Transactions
- Ontology for Scenarios for the Assessment of Automated Vehicles https://www.semanticscholar.org/paper/Ontology-for-Scenarios-for-the-Assessment-of-Gelder-Paardekooper/e8c0b77614e7ff1e398354a2f1a2cac01b9c243c
- Gerrit Bagschik, Till Menzel and Markus Maurerm. Ontology based Scene Creation for the Development of Automated Vehicles. 2018 arxiv
Hybrid Identification Toolbox [HIT], link [not maintained]
Multi-Parametric Toolbox [MPT], link [none native support]
SARXSAT, tool for identifying ARX (autoregressive models with exogenous inputs) and piecewise ARX, Github repo
Tailored for Python
Official
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Tutorial: Jupyter Notebook