Weight-quantized LLMs retain universal approximation up to 1.58 bits with expressive collapse below it and polynomial degradation in capacity as bit count falls.
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A comprehensive survey of edge deep learning in computer vision and medical diagnostics that presents a novel categorization of hardware platforms by performance and usage scenarios.
citing papers explorer
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On the Expressive Power of Weight Quantization in Large Language Models
Weight-quantized LLMs retain universal approximation up to 1.58 bits with expressive collapse below it and polynomial degradation in capacity as bit count falls.
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Edge Deep Learning in Computer Vision and Medical Diagnostics: A Comprehensive Survey
A comprehensive survey of edge deep learning in computer vision and medical diagnostics that presents a novel categorization of hardware platforms by performance and usage scenarios.