arXiv:1904.01681
공식 코드 · Papers with Code
Augmented Neural ODEs
원문 초록
We show that Neural Ordinary Differential Equations (ODEs) learn representations that preserve the topology of the input space and prove that this implies the existence of functions Neural ODEs cannot represent. To address these limitations, we introduce Augmented Neural ODEs which, in addition to being more expressive models, are empirically more stable, generalize better and have a lower computational cost than Neural ODEs.
함께 살펴볼 논문
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Hashed Watermark as a Filter: Defeating Forging and Overwriting Attacks in Weight-based Neural Network Watermarking
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Beyond Accuracy: Metrics that Uncover What Makes a 'Good' Visual Descriptor
Linear Attention with Global Context: A Multipole Attention Mechanism for Vision and Physics
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토론
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