Adaptive Quantization

Adaptive quantization optimizes the precision of numerical representations in machine learning models, aiming to reduce computational costs and memory footprint without significant performance loss. Current research focuses on developing dynamic quantization schemes that adjust bit-widths based on data characteristics or model layer sensitivity, often employing techniques like Gumbel-softmax, k-means clustering, and mixed-precision quantization within architectures such as transformers and convolutional neural networks. This work is significant for deploying large models on resource-constrained devices and improving the efficiency of various applications, including image processing, natural language processing, and speaker verification.