An Ode to an ODE

19 Jun 2020Krzysztof ChoromanskiJared Quincy DavisValerii LikhosherstovXingyou SongJean-Jacques SlotineJacob VarleyHonglak LeeAdrian WellerVikas Sindhwani

We present a new paradigm for Neural ODE algorithms, called ODEtoODE, where time-dependent parameters of the main flow evolve according to a matrix flow on the orthogonal group O(d). This nested system of two flows, where the parameter-flow is constrained to lie on the compact manifold, provides stability and effectiveness of training and provably solves the gradient vanishing-explosion problem which is intrinsically related to training deep neural network architectures such as Neural ODEs... (read more)

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