4 papers
A Deep Learning Framework for Sequence Mining with Bidirectional LSTM and Multi-Scale Attention
Tao Yang, Yu Cheng, Yaokun Ren +3
This paper addresses the challenges of mining latent patterns and modeling contextual dependencies in complex sequence data. A sequence pattern mining algorithm is proposed by inte…
Addressing Class Imbalance with Probabilistic Graphical Models and Variational Inference
Yujia Lou, Jie Liu, Yuan Sheng +3
This study proposes a method for imbalanced data classification based on deep probabilistic graphical models (DPGMs) to solve the problem that traditional methods have insufficient…
A Deep Learning Framework for Boundary-Aware Semantic Segmentation
Tai An, Weiqiang Huang, Da Xu +3
As a fundamental task in computer vision, semantic segmentation is widely applied in fields such as autonomous driving, remote sensing image analysis, and medical image processing.…
Context-Aware Rule Mining Using a Dynamic Transformer-Based Framework
Jie Liu, Yiwei Zhang, Yuan Sheng +3
This study proposes a dynamic rule data mining algorithm based on an improved Transformer architecture, aiming to improve the accuracy and efficiency of rule mining in a dynamic da…