(no subject)

[am]

M.Innes, et al. (2019)
"dP: A Differentiable
Programming System
to Bridge ML and
Scientific Computing."

See also:
A.G. Baydin, B.A. Pearlmutter,
A.A. Radul, J.M. Siskind (2018)
"Automatic differentiation in
machine learning: a survey."
J. of M.L. Res., 18:1-43.

F.d.A. Belbute-Peres, K.A. Smith, K.R.
Allen, J.B. Tenenbaum, J.Z. Kolter
(2018) "End-to-end differentiable
physics for learning and control."
In Adv. in N.I.P.S., p.:7178-7189.

J. Degrave, M. Hermans, J. Dambre,
F. wyffels (2019) "A differentiable
physics engine for deep learning in
robotics." Front. in Neurorob., 13.

E. Dupont, A. Doucet, Y. Whye Teh
(2019) "Augmented Neural ODEs."

Cf. "Neural ODE".