Advanced Optimal Tracking Integrating a Neural Critic Technique for Asymmetric Constrained Zero-Sum Games

Academic Report: Advanced Optimal Tracking Integrating Neural Critic Technique for Asymmetric Constrained Zero-Sum Games Background and Research Problem In the field of modern control, game theory is the mathematical model that studies the competition and cooperation between intelligent decision-makers, involving an interaction decision problem wit...

Structure Enhanced Prototypical Alignment for Unsupervised Cross-Domain Node Classification

Structurally Enhanced Prototype Alignment for Unsupervised Cross-Domain Node Classification Introduction With the advancement of modern information technology, Graph Neural Networks (GNNs) have demonstrated significant success in handling complex network node classification tasks. However, one key challenge is the need for a large amount of high-qu...

Unsupervised Domain Adaptive Segmentation Algorithm Based on Two-Level Category Alignment

Unsupervised Domain Adaptive Segmentation Algorithm Based on Two-Level Category Alignment

Semantic segmentation aims to predict category labels for each pixel in an image (Liu et al., 2021; Wang et al., 2021) and is widely used in scene understanding, medical image analysis, autonomous driving, geographic information systems, and augmented reality (Strudel et al., 2021; Sun et al., 2023). Although the development of deep neural networks...

Prototype-Based Sample-Weighted Distillation Unified Framework Adapted to Missing Modality Sentiment Analysis

Prototype-Based Sample-Weighted Distillation Unified Framework Adapted to Missing Modality Sentiment Analysis

Application of a Prototype-Based Sample Weighted Distillation Unified Framework in Missing Modality Sentiment Analysis Research Background Sentiment analysis is a significant field in Natural Language Processing (NLP). With the development of social media platforms, people increasingly tend to express their emotions through short video clips. This ...

Efficient Tensor Decomposition-Based Filter Pruning

Background Introduction Network Pruning is a crucial technique for designing efficient Convolutional Neural Network (CNN) models. By reducing memory footprint and computational demands, while maintaining or improving overall performance, it makes deploying CNNs on resource-constrained devices (such as mobile phones or embedded systems) feasible. Th...