# Polarity is all you need to learn and transfer faster

Source: [Polarity is all you need to learn and transfer faster](<https://www.sota2.com/research/paper/68fa5f421e209131ce9e87f3>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T19:11:53.560Z

Authors: Qingyang Wang, Michael A.Powell, Ali Geisa, Eric W. Bridgeford, Joshua T. Vogelstein

Published: 2023-03-29T14:43:04.000Z

## Abstract

Natural intelligences (NIs) thrive in a dynamic world - they learn quickly, sometimes with only a few samples. In contrast, artificial intelligences (AIs) typically learn with a prohibitive number of training samples and computational power. What design principle difference between NI and AI could contribute to such a discrepancy? Here, we investigate the role of weight polarity: development processes initialize NIs with advantageous polarity configurations; as NIs grow and learn, synapse magnitudes update, yet polarities are largely kept unchanged. We demonstrate with simulation and image classification tasks that if weight polarities are adequately set a priori, then networks learn with less time and data. We also explicitly illustrate situations in which a priori setting the weight polarities is disadvantageous for networks. Our work illustrates the value of weight polarities from the perspective of statistical and computational efficiency during learning.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2303.17589>)

[PDF](<https://arxiv.org/pdf/2303.17589.pdf>)