Explainable reinforcement learning (XRL) is an emerging subfield of explainable machine
learning that has attracted considerable attention in recent years. The goal of XRL is to …

Deep learning (DL) has recently changed the development of intelligent systems and is
widely adopted in many real-life applications. Despite their various benefits and potentials …

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

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

At the heart of foundation models is the philosophy of" more is different", exemplified by the
astonishing success in computer vision and natural language processing. However, the …

Light-weight convolutional neural networks (CNNs) suffer performance degradation as their
low computational budgets constrain both the depth (number of convolution layers) and the …

The binary neural network, largely saving the storage and computation, serves as a
promising technique for deploying deep models on resource-limited devices. However, the …

Real-world applications require the classification model to adapt to new classes without
forgetting old ones. Correspondingly, Class-Incremental Learning (CIL) aims to train a …

Weight and activation binarization is an effective approach to deep neural network
compression and can accelerate the inference by leveraging bitwise operations. Although …

Post-training quantization (PTQ) is a neural network compression technique that converts a
full-precision model into a quantized model using lower-precision data types. Although it can …