Academic Background Deep Reinforcement Learning (DRL) has achieved remarkable success in various fields such as gaming, robotics, navigation, computer vision, and finance. However, existing DRL algorithms generally suffer from low sample efficiency, requiring vast amounts of data and training steps to achieve desired performance. Particularly in co...

Research on Abnormal Behavior Detection in Surveillance Video Based on Probabilistic Memory Auto-Encoding Network Academic Background In intelligent surveillance systems, abnormal behavior detection is a crucial function widely applied in anti-terrorism, social stability maintenance, and public safety assurance. However, a core challenge in abnorma...

Research on Weight Initialization Method for Restricted Boltzmann Machines Based on Statistical Mechanical Analysis Academic Background In deep learning, the initialization of neural network weights significantly impacts the training effectiveness of models. Particularly in feed-forward neural networks, several dataset-free weight initialization me...

Research on Zero-Shot Human-Object Interaction Detection Based on Vision-Language Integration Academic Background Human-Object Interaction (HOI) detection is an important research direction in the field of computer vision, aiming to identify interactions between humans and objects in images. Traditional HOI detection methods primarily rely on super...

Efficient Private Inference in Deep Neural Networks: Breakthrough Research on the PrivCore Framework Background Introduction With the rapid development of deep learning technology, Deep Neural Networks (DNNs) have been increasingly applied in fields such as image recognition, natural language processing, and medical diagnosis. However, as the deman...

Academic Background With the increasing complexity of artificial intelligence (AI) models and the growing demand for data privacy protection, Federated Learning (FL) has emerged as a hot research topic as a distributed machine learning paradigm. Federated Learning allows multiple clients to collaboratively train a global model without sharing local...