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

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

High-resolution dense prediction enables many appealing real-world applications, such as
computational photography, autonomous driving, etc. However, the vast computational cost …

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

Self-driving cars need to understand 3D scenes efficiently and accurately in order to drive
safely. Given the limited hardware resources, existing 3D perception models are not able to …

The attention mechanism is becoming increasingly popular in Natural Language Processing
(NLP) applications, showing superior performance than convolutional and recurrent …

Deep learning (DL) techniques have obtained remarkable achievements on various tasks,
such as image recognition, object detection, and language modeling. However, building a …

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

Quantum noise is the key challenge in Noisy Intermediate-Scale Quantum (NISQ)
computers. Previous work for mitigating noise has primarily focused on gate-level or pulse …

Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial
intelligence (AI), including computer vision, natural language processing, and speech …