"Learning Distributed Representations of Concepts" by Geoffrey Hinton

In this influential paper, Hinton explores how neural networks internally encode abstract ideas, concepts, and relationships through patterns of neural activity. Hinton describes using the backpropagation algorithm to fine-tune connections between neurons, allowing networks to implicitly learn meaningful representations without relying on explicit symbolic structures.

This pioneering idea laid critical groundwork for modern deep learning, significantly advancing our understanding of how neural networks generalize knowledge from limited data.
附件(907KB) buted_Representations_of_Concepts(Geoffrey_Hinton,_1986).pdf
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