Accurate sleep staging is crucial for the diagnosis of diseases such as sleep disorders.
Existing sleep staging models with excellent performance are usually large and require a lot …

Crowd density estimation has gained significant research interest owing to its potential in
various industries and social applications. Therefore, this article proposes a multistyle joint …

Abstract Knowledge distillation is an attractive approach for learning compact deep neural
networks, which learns a lightweight student model by distilling knowledge from a complex …

Deep learning has achieved notable success across academia, medicine, and industry. Its
ability to identify complex patterns in large-scale data and to manage millions of parameters …

This work presents a Fully BInarized Large Language Model (FBI-LLM), demonstrating for
the first time how to train a large-scale binary language model from scratch (not the partial …

Many recent methods aim to merge neural networks (NNs) with identical architectures
trained on different tasks to obtain a single multi-task model. Most existing works tackle the …

The goal of face attribute recognition (FAR) is to recognize the attributes of face images,
such as gender, race, etc. Multi-dataset fusion aims to train a network with multiple datasets …

The significance of mental health classification is paramount in contemporary society, where
digital platforms serve as crucial sources for monitoring individuals' well-being. However …

Abstract Knowledge distillation is a technique that aims to distill the knowledge from a large
well-trained teacher model to a lightweight student model. In recent years, multi-teacher …

Quantization-aware training (QAT) and Knowledge Distillation (KD) are combined to achieve
competitive performance in creating low-bit deep learning models. However, existing works …