Categories: FAANG

Compute-Optimal Quantization-Aware Training

Quantization-aware training (QAT) is a leading technique for improving the accuracy of quantized neural networks. Previ-
ous work has shown that decomposing training into a full-precision (FP) phase followed by a QAT phase yields superior
accuracy compared to QAT alone. However, the optimal allocation of compute between the FP and QAT phases remains
unclear. We conduct extensive experiments with various compute budgets, QAT bit widths, and model sizes from 86.0M
to 2.2B to investigate how different QAT durations impact final performance. We demonstrate that, contrary to previous
findings, the…
AI Generated Robotic Content

Recent Posts

What tools would you use to make morphing videos like this?

submitted by /u/nikitagent [link] [comments]

13 hours ago

Bias after Prompting: Persistent Discrimination in Large Language Models

A dangerous assumption that can be made from prior work on the bias transfer hypothesis…

13 hours ago

Post-Training Generative Recommenders with Advantage-Weighted Supervised Finetuning

Author: Keertana Chidambaram, Qiuling Xu, Ko-Jen Hsiao, Moumita Bhattacharya(*The work was done when Keertana interned…

13 hours ago

When your AI browser becomes your enemy: The Comet security disaster

Remember when browsers were simple? You clicked a link, a page loaded, maybe you filled…

14 hours ago

Baseus Inspire XC1 Review: Excellent Open Earbuds

These affordable open buds come with Bose-crafted sound.

14 hours ago

DeepMind introduces AI agent that learns to complete various tasks in a scalable world model

Over the past decade, deep learning has transformed how artificial intelligence (AI) agents perceive and…

14 hours ago