Search Results for author:
Found 11 papers, 3 papers with code
Research on Key Technologies for Cross-Cloud Federated Training of Large Language Models
With the rapid development of natural language processing technology, large language models have demonstrated exceptional performance in various application scenarios.
TRIZ Method for Urban Building Energy Optimization: GWO-SARIMA-LSTM Forecasting model
The main contribution of this research is the development of a robust model that leverages the strengths of TRIZ and advanced deep learning techniques, improving the accuracy of energy consumption predictions.
Balancing Innovation and Privacy: Data Security Strategies in Natural Language Processing Applications
By introducing a differential privacy mechanism, our model ensures the accuracy and reliability of data analysis results while adding random noise.
Applying Hybrid Graph Neural Networks to Strengthen Credit Risk Analysis
This paper presents a novel approach to credit risk prediction by employing Graph Convolutional Neural Networks (GCNNs) to assess the creditworthiness of borrowers.
Toward Pedestrian Head Tracking: A Benchmark Dataset and an Information Fusion Network
Existing head datasets have limited coverage of complex pedestrian flows and scenes (e. g., pedestrian interactions, occlusions, and object interference).
Multi-Scenario Combination Based on Multi-Agent Reinforcement Learning to Optimize the Advertising Recommendation System
This paper explores multi-scenario optimization on large platforms using multi-agent reinforcement learning (MARL).
Research on Driver Facial Fatigue Detection Based on Yolov8 Model
In a society where traffic accidents frequently occur, fatigue driving has emerged as a grave issue.
SPA: Towards A Computational Friendly Cloud-Base and On-Devices Collaboration Seq2seq Personalized Generation with Casual Inference
Large language models(LLMs) have shown its outperforming ability on various tasks and question answering.
Application Of Vision-Language Models For Assessing Osteoarthritis Disease Severity
Osteoarthritis (OA) poses a global health challenge, demanding precise diagnostic methods.
Disentangle-based Continual Graph Representation Learning
Graph embedding (GE) methods embed nodes (and/or edges) in graph into a low-dimensional semantic space, and have shown its effectiveness in modeling multi-relational data.
Exploiting Contextual Information via Dynamic Memory Network for Event Detection
Meanwhile, dynamic memory network (DMN) has demonstrated promising capability in capturing contextual information and has been applied successfully to various tasks.
