Learning with Quantization, Polar Quantizer, and Secure Source Coding

ePrint Report: Learning with Quantization, Polar Quantizer, and Secure Source Coding

Shanxiang Lyu, Ling Liu, Cong Ling

This paper presents a generalization of the Learning With Rounding (LWR) problem, initially introduced by Banerjee, Peikert, and Rosen, by applying the perspective of vector quantization. In LWR, noise is induced by rounding each coordinate to the nearest multiple of a fraction, a process inherently tied to scalar quantization. By considering a new variant termed Learning With Quantization (LWQ), we explore large-dimensional fast-decodable …

coding eprint report report