Mike Innes

According to our database, Mike Innes authored at least 7 papers between 2018 and 2020.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2020

WordTokenizers.jl: Basic tools for tokenizing natural language in Julia.

Ayush Kaushal, Lyndon White, Mike Innes, Rohit Kumar

J. Open Source Softw., 2020

Sense & Sensitivities: The Path to General-Purpose Algorithmic Differentiation.

Mike Innes

Proceedings of the Third Conference on Machine Learning and Systems, 2020

Generalized Physics-Informed Learning through Language-Wide Differentiable Programming.

Christopher Rackauckas, Alan Edelman, Keno Fischer, Mike Innes, Elliot Saba, Viral B. Shah, Will Tebbutt

Proceedings of the AAAI 2020 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 23rd - to, 2020

2019

A Differentiable Programming System to Bridge Machine Learning and Scientific Computing.

Mike Innes, Alan Edelman, Keno Fischer, Christopher Rackauckas, Elliot Saba, Viral B. Shah, Will Tebbutt

CoRR, 2019

DiffEqFlux.jl - A Julia Library for Neural Differential Equations.

Christopher Rackauckas, Mike Innes, Yingbo Ma, Jesse Bettencourt, Lyndon White, Vaibhav Dixit

CoRR, 2019

2018

Flux: Elegant machine learning with Julia.

Mike Innes

J. Open Source Softw., 2018

A Comparison of Automatic Differentiation and Continuous Sensitivity Analysis for Derivatives of Differential Equation Solutions.

Christopher Rackauckas, Yingbo Ma, Vaibhav Dixit, Xingjian Guo, Mike Innes, Jarrett Revels, Joakim Nyberg, Vijay Ivaturi

CoRR, 2018