Princeton University Researchers Introduce ‘DataMUX’: A Technique That Enables Deep Neural Networks to Process Multiple Inputs Simultaneously Using a Single Compact Representation

A Princeton University research team proposed Data Multiplexing for Neural Networks in their new study DataMUX: Data Multiplexing for Neural Networks (DataMUX). This new method allows neural networks to analyze several inputs simultaneously and make correct predictions, improving model throughput while requiring minimal additional memory. 

Because of its unique capacity to represent complicated real-life issues, deep neural networks (DNNs) are a famous architecture in the machine learning field. Recent research suggests that such networks are grossly overparameterized, necessitating significant and …

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