Abstract Post-training quantization (PTQ), which only requires a tiny dataset for calibration
without end-to-end retraining, is a light and practical model compression technique …

Abstract Vision Transformers (ViTs) have achieved state-of-the-art performance on various
computer vision applications. However, these models have considerable storage and …

Structured pruning and quantization are promising approaches for reducing the inference
time and memory footprint of neural networks. However, most existing methods require the …

To obtain lower inference latency and less memory footprint of deep neural networks, model
quantization has been widely employed in deep model deployment, by converting the …

Neural models are equivalent to dynamic systems from a physics-inspired view, implying
that computation on neural networks can be interpreted as the dynamical interactions …

The disparity in healthcare personnel expertise and medical resources across different
regions of the world is a pressing social issue. Artificial intelligence technology offers new …

Diffusion models have achieved great success in image generation tasks through iterative
noise estimation. However, the heavy denoising process and complex neural networks …