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cs.LG2025
Provable Post-Training Quantization: Theoretical Analysis of OPTQ and Qronos
Haoyu Zhang, Shihao Zhang, Ian Colbert +1
Post-training quantization (PTQ) has become a crucial tool for reducing the memory and compute costs of modern deep neural networks, including large language models (LLMs). Among P…
cs.LG2025
Qronos: Correcting the Past by Shaping the Future... in Post-Training Quantization
Shihao Zhang, Haoyu Zhang, Ian Colbert +1
We introduce Qronos -- a new state-of-the-art post-training quantization algorithm that sequentially rounds and updates neural network weights. Qronos not only explicitly corrects…
cs.LG2024
Unified Stochastic Framework for Neural Network Quantization and Pruning
Haoyu Zhang, Rayan Saab
Quantization and pruning are two essential techniques for compressing neural networks, yet they are often treated independently, with limited theoretical analysis connecting them.…