The main goal of machine learning is the creation of self-learning algorithms in many areas
of human activity. It allows a replacement of a person with artificial intelligence in seeking to …

This paper presents an extensive literature review on Binary Neural Network (BNN). BNN
utilizes binary weights and activation function parameters to substitute the full-precision …

Network binarization emerges as one of the most promising compression approaches
offering extraordinary computation and memory savings by minimizing the bit-width …

Abstract Optimization of Top-1 ImageNet promotes enormous networks that may be
impractical in inference settings. Binary neural networks (BNNs) have the potential to …

This paper explores network binarization, a radical form of quantization, compressing model
weights to a single bit, specifically for Large Language Models (LLMs) compression. Due to …

The fields of both Natural Language Processing (NLP) and Automated Machine Learning
(AutoML) have achieved remarkable results over the past years. In NLP, especially Large …

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held
at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition …

As the scales of neural networks increase, techniques that enable them to run with low
computational cost and energy efficiency are required. From such demands, various efficient …

There has been a great deal of recent interest in binarized neural networks, especially
because of their explainability. At the same time, automatic differentiation algorithms such as …

The union of Edge Computing (EC) and Artificial Intelligence (AI) has brought forward the
Edge AI concept to provide intelligent solutions close to the end-user environment, for …