Over the past decade, deep-learning-based representations have demonstrated remarkable
performance in academia and industry. The learning capability of convolutional neural …

Deep Neural Networks (DNNs) are very popular because of their high performance in
various cognitive tasks in Machine Learning (ML). Recent advancements in DNNs have …

We consider the problem of model compression for deep neural networks (DNNs) in the
challenging one-shot/post-training setting, in which we are given an accurate trained model …

This chapter provides approaches to the problem of quantizing the numerical values in deep
Neural Network computations, covering the advantages/disadvantages of current methods …

While neural networks have advanced the frontiers in many applications, they often come at
a high computational cost. Reducing the power and latency of neural network inference is …

Deep neural networks have been applied in many applications exhibiting extraordinary
abilities in the field of computer vision. However, complex network architectures challenge …

Recently, transformer has achieved remarkable performance on a variety of computer vision
applications. Compared with mainstream convolutional neural networks, vision transformers …

Transformer models have been widely adopted in various domains over the last years and
especially large language models have advanced the field of AI significantly. Due to their …

Denoising diffusion (score-based) generative models have recently achieved significant
accomplishments in generating realistic and diverse data. These approaches define a …

We study the challenging task of neural network quantization without end-to-end retraining,
called Post-training Quantization (PTQ). PTQ usually requires a small subset of training data …