26 citations · 98 across the 31 of their papers we have counts for
5 papers · 1 filter
Beacon: Post-Training Quantization with Integrated Grid Selection
Shihao Zhang, Rayan Saab
Quantization is a widely used compression technique for reducing the memory and computation costs of large pre-trained models. A key challenge in per-channel post-training quantiza…
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…
Theoretical Guarantees for Low-Rank Compression of Deep Neural Networks
Shihao Zhang, Rayan Saab
Deep neural networks have achieved state-of-the-art performance across numerous applications, but their high memory and computational demands present significant challenges, partic…