This paper critically examines model compression techniques within the machine learning
(ML) domain, emphasizing their role in enhancing model efficiency for deployment in …

Facial beauty prediction (FBP) is a burgeoning issue for attractiveness evaluation, which
aims to make assessment consistent with human opinion. Since FBP is a regression …

Predictive maintenance (PdM) has emerged as a prominent strategy that can recognize the
current state and predict the future trend of machines. It helps prevent disastrous …

Network compression methods minimize the number of network parameters and
computation costs while maintaining desired network performance. However, the safety …

Autonomous vehicles include driverless, self-driving and robotic cars, and other platforms
capable of sensing and interacting with its environment and navigating without human help …

In the coming Industry 4.0 era, IoT (Internet of Things) technology plays a more and more
critical role. Anomaly detection is one of the essential applications for ensuring safe …

In this paper, we propose a concept of approximate bisimulation relation for feedforward
neural networks. In the framework of approximate bisimulation relation, a novel neural …

We studied the quantization of neural networks for their compression and representation
without retraining. The goal is to facilitate neural network representation and deployment in …

In this paper, we propose a method of repairing compressed Feedforward Neural Networks
(FNNs) based on equivalence evaluation of two neural networks. In the repairing framework …

Knowledge distillation (KD) is one of popular algorithms for compressing deep neural
networks because it generates a compact but still powerful deep neural network for the …