11 papers
BaKron: Efficient Quantization with Kronecker-Factored Hessians
Johann Birnick, Rayan Saab
We accelerate a family of algorithms for neural network quantization whose geometry is informed by any Kronecker-factored approximation of the Hessian. GPTQ-style adaptive rounding…
GPTQ-intrinsic LoRA: A Near-optimal Algorithm for Low-precision Quantization with Low-rank Adaptation
Shihao Zhang, Rayan Saab
Post-training quantization is widely used for compressing large neural networks, but aggressive low-bit quantization can significantly degrade model quality. A common remedy is to…
The Measure of Deception: An Analysis of Data Forging in Machine Unlearning
Rishabh Dixit, Yuan Hui, Rayan Saab
Motivated by privacy regulations and the need to mitigate the effects of harmful data, machine unlearning seeks to modify trained models so that they effectively ``forget'' designa…
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…
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…
Low-Bit Quantization of Bandlimited Graph Signals via Iterative Methods
Felix Krahmer, He Lyu, Rayan Saab +3
We study the quantization of real-valued bandlimited signals on graphs, focusing on low-bit representations. We propose iterative noise-shaping algorithms for quantization, includi…