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Found 4 papers, 3 papers with code
A separability-based approach to quantifying generalization: which layer is best?
Which layers of a network are likely to generalize best?
Learning dynamic representations of the functional connectome in neurobiological networks
We introduce an unsupervised approach to learn the dynamic affinities between neurons in live, behaving animals, and to reveal which communities form among neurons at different times.
Zero-shot generalization across architectures for visual classification
Generalization to unseen data is a key desideratum for deep networks, but its relation to classification accuracy is unclear.
IAN: Iterated Adaptive Neighborhoods for manifold learning and dimensionality estimation
Invoking the manifold assumption in machine learning requires knowledge of the manifold's geometry and dimension, and theory dictates how many samples are required.
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