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Department of Mathematics,
Department of Mathematics,
University of California San Diego
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Math 278B: Mathematics of Information, Data, and Signals
Johann Birnick
UC San Diego
Neural Network Quantization with Babai’s Nearest Plane Algorithm
Abstract:
Quantization is the art of converting neural network weights to low precision, say to just 1–4 bits per scalar, while keeping the network accuracy high. We study one natural and popular way of doing this, which consists of choosing a surrogate loss that is quadratic in the parameters, and then applying a simple integer optimization algorithm to this loss. In particular, we discuss how the second step (integer optimization algorithm) influences the first step (information-theoretically optimal choice of the surrogate loss).
Host: Rob Webber
October 29, 2026
11:00 AM
APM 2402
Research Areas
Mathematics of Information, Data, and Signals****************************
