no code implementations • 27 May 2022 • Rym Jaroudi, Lukáš Malý, Gabriel Eilertsen, B. Tomas Johansson, Jonas Unger, George Baravdish
This paper presents the Standalone Neural ODE (sNODE), a continuous-depth neural ODE model capable of describing a full deep neural network.
no code implementations • 11 Feb 2022 • George Baravdish, Gabriel Eilertsen, Rym Jaroudi, B. Tomas Johansson, Lukáš Malý, Jonas Unger
The inverse problem of supervised reconstruction of depth-variable (time-dependent) parameters in a neural ordinary differential equation (NODE) is considered, that means finding the weights of a residual network with time continuous layers.
