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\n  \n 2023\n \n \n (1)\n \n \n
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\n  \n Proceedings of international conference\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Input uncertainty propagation through trained neural networks.\n \n \n \n \n\n\n \n Monchot, P.; Coquelin, L.; Petit, S.; Marmin, S.; Le Pennec, E.; and Fischer, N.\n\n\n \n\n\n\n In International Conference on Machine Learning, 2023. \n \n\n\n\n
\n\n\n\n \n \n \"Input pdf\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{monchot23,\n  author          = {Monchot, P. and Coquelin, L. and Petit, S. and Marmin, S. and Le Pennec, E. and Fischer, N.},\n  booktitle       = {International Conference on Machine Learning},\n  title           = {Input uncertainty propagation through trained neural networks},\n  year            = {2023},\n  keywords =\t {Proceedings, Causality, ThemeCausality},\n  pubtype =\t {Proceedings of international conference},\n  subject =\t {Causality},\n    url_pdf = {http://lepennec.perso.math.cnrs.fr/Preprint/Causality/2023-ICML-MCPMLP.pdf},\n  lang = {english}\n}\n\n
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\n \n\n \n \n \n \n \n \n Causal and Interpretable Rules for Time Series Analysis.\n \n \n \n \n\n\n \n Dhaou, A.; Bertoncello, A.; Gourvénec, S.; Garnier, J.; and Le Pennec, E.\n\n\n \n\n\n\n In Knowledge Discovery and Data Mining, pages 2764–2772, 2021. \n \n\n\n\n
\n\n\n\n \n \n \"Causal pdf\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{Dhaou21,\n  author          = {Dhaou, A. and Bertoncello, A. and Gourvénec, S. and Garnier, J. and Le Pennec, E.},\n  title           = {Causal and Interpretable Rules for Time Series Analysis},\n  booktitle       = {Knowledge Discovery and Data Mining},\n  year            = {2021},\n  doi             = {doi/10.1145/3447548.3467161},\n  pubtype =\t {Proceedings of international conference},\n  keywords =\t {Proceedings, Causality, ThemeCausality},\n  x-international-audience ={yes},\n  x-proceedings ={yes},\n  subject = {Causality},\n  x-invited-conference ={no},\n  pages = {2764–2772},\n  url_PDF = {http://lepennec.perso.math.cnrs.fr/Reprint/Causality/2021-KDD-DBGGLP.pdf},\n  lang =\t {English}\n}\n\n
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