3 papers
cs.LG2026
Why Does Post-Training Quantization Work?
Yuxiang Chen, Michael Beyer, Jun Zhu +1
Post-training quantization compresses large language models (LLMs) by storing their weights at reduced precision, and each quantized weight introduces an error into the hidden stat…
cs.LG2025
TetraJet-v2: Accurate NVFP4 Training for Large Language Models with Oscillation Suppression and Outlier Control
Yuxiang Chen, Yifan Liu, Xiaoming Xu +5
Large Language Models (LLMs) training is prohibitively expensive, driving interest in low-precision fully-quantized training (FQT). While novel 4-bit formats like NVFP4 offer subst…
cs.LG2020
Fault Injectors for TensorFlow: Evaluation of the Impact of Random Hardware Faults on Deep CNNs
Michael Beyer, Andrey Morozov, Emil Valiev +4
Today, Deep Learning (DL) enhances almost every industrial sector, including safety-critical areas. The next generation of safety standards will define appropriate verification tec…