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New 'knowledge matrices' offer higher representation for neural networks

By PulseAugur Editorial · [1 sources] ·

This paper introduces "knowledge matrices" as a novel way to represent information within trained feedforward neural networks. These matrices, derived from network weights and activations, offer a higher-level representation than traditional hidden activations. The research demonstrates that knowledge matrices can be used to analyze network behavior, measure distances between different architectures like ResNet-152 and DenseNet-121, and even quantify the impact of adversarial attacks. AI

IMPACT Introduces a new theoretical framework for understanding and comparing neural network representations.

RANK_REASON Academic paper introducing a new theoretical concept for analyzing neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New 'knowledge matrices' offer higher representation for neural networks

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Academic paper introducing a new theoretical concept for analyzing neural networks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Marco Armenta ·

    Hidden Activations are not Enough I: Knowledge Matrices as Higher Representations

    We study the knowledge matrix of a trained feedforward network as a higher representation of its inputs. A network is a pair $(W,f)$, a thin representation $W$ of its quiver and an activation $f$; its function factorizes through the space of quiver representations, each input $x$…

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