ANODE: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs

27 Feb 2019Amir GholamiKurt KeutzerGeorge Biros

Residual neural networks can be viewed as the forward Euler discretization of an Ordinary Differential Equation (ODE) with a unit time step. This has recently motivated researchers to explore other discretization approaches and train ODE based networks... (read more)

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