The remarkable performance of deep Convolutional neural networks (CNNs) is generally
attributed to their deeper and wider architectures, which can come with significant …

Recent advancements in machine learning achieved by Deep Neural Networks (DNNs)
have been significant. While demonstrating high accuracy, DNNs are associated with a …

Generative Large Language Models (LLMs) have demonstrated remarkable results for a
wide range of tasks. However, deploying these models for inference has been a significant …

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

Recent advances in state-of-the-art DNN architecture design have been moving toward
Transformer models. These models achieve superior accuracy across a wide range of …

Lung cancer, also known as pulmonary cancer, is one of the deadliest cancers, but yet
curable if detected at the early stage. At present, the ambiguous features of the lung cancer …

Transformer-based language models have become a key building block for natural
language processing. While these models are extremely accurate, they can be too large and …

Quantization of deep neural networks (DNN) has been proven effective for compressing and
accelerating DNN models. Data-free quantization (DFQ) is a promising approach without the …

In this community review report, we discuss applications and techniques for fast machine
learning (ML) in science—the concept of integrating powerful ML methods into the real-time …

Transformers yield state-of-the-art results across many tasks. However, their heuristically
designed architecture impose huge computational costs during inference. This work aims on …