Time Dependence in Non-Autonomous Neural ODEs

5 May 2020Jared Quincy DavisKrzysztof ChoromanskiJake VarleyHonglak LeeJean-Jacques SlotineValerii LikhosterovAdrian WellerAmeesh MakadiaVikas Sindhwani

Neural Ordinary Differential Equations (ODEs) are elegant reinterpretations of deep networks where continuous time can replace the discrete notion of depth, ODE solvers perform forward propagation, and the adjoint method enables efficient, constant memory backpropagation. Neural ODEs are universal approximators only when they are non-autonomous, that is, the dynamics depends explicitly on time... (read more)

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