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Neural Ordinary Differential Equations (Neural ODEs) : Implementation
Neural Ordinary Differential Equations (Neural ODEs) are a class of machine learning models that treat the transformation of data through a neural network as a continuous-time dynamical system, rather than as a sequence of discrete layers. Introduced by Chen et al. in 2018, they represent a novel approach to learning and optimization, combining ...
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