Automatic Differentiation in PyTorch

NIPS 2017 2017 Adam PaszkeSam GrossSoumith ChintalaGregory ChananEdward YangZachary DeVitoZeming LinAlban DesmaisonLuca AntigaAdam Lerer

In this article, we describe an automatic differentiation module of PyTorch — a library designed to enable rapid research on machine learning models. It builds upon a few projects, most notably Lua Torch, Chainer, and HIPS Autograd, and provides a high performance environment with easy access to automatic differentiation of models executed on different devices (CPU and GPU)... (read more)

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