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Joshua Hanson
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Journal Articles
- 2021
- [j1]Joshua Hanson
, Pavel B. Bochev, Biliana S. Paskaleva:
Learning compact physics-aware delayed photocurrent models using dynamic mode decomposition. Stat. Anal. Data Min. 14(6): 521-535 (2021)
Conference and Workshop Papers
- 2024
- [c5]Joshua Hanson, Maxim Raginsky:
Rademacher complexity of neural ODEs via Chen-Fliess series. L4DC 2024: 758-769 - 2022
- [c4]Joshua Hanson, Maxim Raginsky:
Fitting an immersed submanifold to data via Sussmann's orbit theorem. CDC 2022: 5323-5328 - 2021
- [c3]Joshua Hanson, Maxim Raginsky, Eduardo D. Sontag:
Learning Recurrent Neural Net Models of Nonlinear Systems. L4DC 2021: 425-435 - 2020
- [c2]Joshua Hanson
, Maxim Raginsky:
Universal Simulation of Stable Dynamical Systems by Recurrent Neural Nets. L4DC 2020: 384-392 - 2019
- [c1]Joshua Hanson, Maxim Raginsky:
Universal Approximation of Input-Output Maps by Temporal Convolutional Nets. NeurIPS 2019: 14048-14058
Informal and Other Publications
- 2024
- [i6]Joshua Hanson, Maxim Raginsky:
Rademacher Complexity of Neural ODEs via Chen-Fliess Series. CoRR abs/2401.16655 (2024) - [i5]Joshua Hanson, Biliana S. Paskaleva, Pavel B. Bochev:
Non-intrusive data-driven model order reduction for circuits based on Hammerstein architectures. CoRR abs/2405.20178 (2024) - 2022
- [i4]Joshua Hanson, Maxim Raginsky:
Fitting an immersed submanifold to data via Sussmann's orbit theorem. CoRR abs/2204.01119 (2022) - 2020
- [i3]Joshua Hanson
, Pavel B. Bochev, Biliana S. Paskaleva:
Learning Compact Physics-Aware Delayed Photocurrent Models Using Dynamic Mode Decomposition. CoRR abs/2008.12319 (2020) - [i2]Joshua Hanson
, Maxim Raginsky, Eduardo D. Sontag:
Learning Recurrent Neural Net Models of Nonlinear Systems. CoRR abs/2011.09573 (2020) - 2019
- [i1]Joshua Hanson
, Maxim Raginsky:
Universal Approximation of Input-Output Maps by Temporal Convolutional Nets. CoRR abs/1906.09211 (2019)
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