Port-Hamiltonian Approach to Neural Network Training

6 Sep 2019Stefano MassaroliMichael PoliFederico CalifanoAngela FaragassoJinkyoo ParkAtsushi YamashitaHajime Asama

Neural networks are discrete entities: subdivided into discrete layers and parametrized by weights which are iteratively optimized via difference equations. Recent work proposes networks with layer outputs which are no longer quantized but are solutions of an ordinary differential equation (ODE); however, these networks are still optimized via discrete methods (e.g. gradient descent)... (read more)

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