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The existing Neural ODE formulation relies on an explicit knowledge of the termination time.
The components of membrane conductance in the giant axon of loligo
Allan L Hodgkin and Andrew F Huxley · 1952
Earlier work this paper cites.
The mathematical theory of optimal processes
Lev Semenovich Pontryagin, EF Mishchenko, VG Boltyanskii, and RV Gamkrelidze · 1962
Earlier work this paper cites.
On state estimation in switching environments
G Ackerson and K Fu · 1970
Earlier work this paper cites.
State estimation for discrete systems with switching parameters
Chaw-Bing Chang and Michael Athans · 1978
Earlier work this paper cites.
A theoretical framework for back-propagation
Yann Le Cun · 1988
Earlier work this paper cites.
Python reference manual
Guido Van Rossum and Fred L Drake Jr · 1995
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Lapicque’s introduction of the integrate-and-fire model neuron (1907)
Larry F Abbott · 1999
Earlier work this paper cites.
An introduction to the adjoint approach to design
Michael B Giles and Niles A Pierce · 2000
Earlier work this paper cites.
Event location for ordinary differential equations
LF Shampine and S Thompson · 2000
Earlier work this paper cites.
Policy gradient methods for reinforcement learning with function approximation
Richard S Sutton, David A McAllester, Satinder P Singh, and Yishay Mansour · 2000
Earlier work this paper cites.
An introduction to the theory of point processes, volume 1: Elementary theory and methods
Daryl J Daley and David Vere-Jones · 2003
Earlier work this paper cites.
Dynamic multidrug therapies for hiv: Optimal and sti control approaches
Brian M Adams, Harvey T Banks, Hee-Dae Kwon, and Hien T Tran · 2004
Earlier work this paper cites.
SUNDIALS: Suite of nonlinear and differential/algebraic equation solvers
Alan C Hindmarsh, Peter N Brown, Keith E Grant, Steven L Lee, Radu Serban, Dan E Shumaker, and Carol S Woodward · 2005
Earlier work this paper cites.
A guide to NumPy , volume 1
Travis E Oliphant · 2006
Earlier work this paper cites.
Matplotlib: A 2d graphics environment
John D Hunter · 2007
Earlier work this paper cites.
Chipmunk physics — http://chipmunk-physics.net/ , 2007
S Lembcke · 2007
Earlier work this paper cites.
Python for scientific computing
Travis E Oliphant · 2007
Earlier work this paper cites.
Quantile mechanics
György Steinbrecher and William T Shaw · 2008
Earlier work this paper cites.
Nonparametric Bayesian learning of switching linear dynamical systems
Emily Fox, Erik B Sudderth, Michael I Jordan, and Alan S Willsky · 2009
Earlier work this paper cites.
pymunk — http://www.pymunk.org/en/latest/ , 2011
V Blomqvist · 2011
Earlier work this paper cites.
The numpy array: a structure for efficient numerical computation
Stefan Van Der Walt, S Chris Colbert, and Gael Varoquaux · 2011
Earlier work this paper cites.
The implicit function theorem: history, theory, and applications
Steven G Krantz and Harold R Parks · 2012
Earlier work this paper cites.
Python for data analysis: Data wrangling with Pandas, NumPy, and IPython
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{ \{ SciPy } \} : Open source scientific tools for { \{ Python } \}
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Latent ODEs for irregularly-sampled time series
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Hydra - a framework for elegantly configuring complex applications
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