The Structure of Neural Embeddings
SMRTR summary
Deep neural networks produce structured embeddings with hierarchical organization, linear feature representation, and superposition of concepts. These latent spaces exhibit properties like manifold structure, universality across models, and vulnerability to adversarial inputs. Extensive training leads to neural collapse, creating distinct class clusters in the final layer.
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