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• W3134579437 abstract "It is well established that training deep neural networks gives useful representations that capture essential features of the inputs. However, these representations are poorly understood in theory and practice. In the context of supervised learning an important question is whether these representations capture features informative for classification, while filtering out non-informative noisy ones. We explore a formalization of this question by considering a generative process where each class is associated with a high-dimensional and different classes define different manifolds. Under this model, each input is produced using two latent vectors: (i) a manifold $gamma$ and; (ii)~a transformation parameter $theta$ that shifts examples along the surface of a manifold. E.g., $gamma$ might represent a canonical image of a dog, and $theta$ might stand for variations in pose, background or lighting. We provide theoretical and empirical evidence that neural representations can be viewed as LSH-like functions that map each input to an embedding that is a function of solely the informative $gamma$ and invariant to $theta$, effectively recovering the identifier $gamma$. An important consequence of this behavior is one-shot learning to unseen classes." @default.
• W3134579437 created "2021-03-15" @default.
• W3134579437 creator A5000758286 @default.
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• W3134579437 date "2021-03-11" @default.
• W3134579437 modified "2023-09-28" @default.
• W3134579437 title "For Manifold Learning, Deep Neural Networks can be Locality Sensitive Hash Functions." @default.
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• W3134579437 hasPublicationYear "2021" @default.
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