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Journal Articles
- 2020
[j2]Ayush Kaushal
, Lyndon White, Mike Innes, Rohit Kumar:
WordTokenizers.jl: Basic tools for tokenizing natural language in Julia. J. Open Source Softw. 5(46): 1956 (2020)- 2018
- [j1]Mike Innes:
Flux: Elegant machine learning with Julia. J. Open Source Softw. 3(25): 602 (2018)
Conference and Workshop Papers
- 2020
- [c2]Christopher Rackauckas, Alan Edelman, Keno Fischer, Mike Innes, Elliot Saba, Viral B. Shah, Will Tebbutt:
Generalized Physics-Informed Learning through Language-Wide Differentiable Programming. AAAI Spring Symposium: MLPS 2020 - [c1]Mike Innes:
Sense & Sensitivities: The Path to General-Purpose Algorithmic Differentiation. MLSys 2020
Informal and Other Publications
- 2019
- [i3]Christopher Rackauckas, Mike Innes, Yingbo Ma, Jesse Bettencourt, Lyndon White, Vaibhav Dixit:
DiffEqFlux.jl - A Julia Library for Neural Differential Equations. CoRR abs/1902.02376 (2019) - [i2]Mike Innes, Alan Edelman, Keno Fischer, Christopher Rackauckas, Elliot Saba, Viral B. Shah, Will Tebbutt:
A Differentiable Programming System to Bridge Machine Learning and Scientific Computing. CoRR abs/1907.07587 (2019) - 2018
- [i1]Christopher Rackauckas, Yingbo Ma, Vaibhav Dixit, Xingjian Guo, Mike Innes, Jarrett Revels, Joakim Nyberg, Vijay Ivaturi:
A Comparison of Automatic Differentiation and Continuous Sensitivity Analysis for Derivatives of Differential Equation Solutions. CoRR abs/1812.01892 (2018)
Coauthor Index
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