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Found 2 papers, 1 papers with code
Estimating Shape Distances on Neural Representations with Limited Samples
Measuring geometric similarity between high-dimensional network representations is a topic of longstanding interest to neuroscience and deep learning.
The Natural Tendency of Feed Forward Neural Networks to Favor Invariant Units
We discuss consequences of this relationship for AI: deep nets naturally weight invariant units over sensitive units, and this can be strengthened with training, perhaps contributing to generalization performance.
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