most citedA Deep Learning Framework for Sequence Mining with Bidirectional LSTM and Multi-Scale Attention

3 citations · 3 across the 4 of their papers we have counts for

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cs.LG2025★ 3 cited

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…

cs.LG2025

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…

cs.LG2025

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…