Sobolev Training for Neural Networks

NeurIPS 2017 Wojciech Marian CzarneckiSimon OsinderoMax JaderbergGrzegorz ŚwirszczRazvan Pascanu

At the heart of deep learning we aim to use neural networks as function approximators - training them to produce outputs from inputs in emulation of a ground truth function or data creation process. In many cases we only have access to input-output pairs from the ground truth, however it is becoming more common to have access to derivatives of the target output with respect to the input - for example when the ground truth function is itself a neural network such as in network compression or distillation... (read more)

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