Novel Approach
This research explores novel approaches across diverse fields, aiming to improve existing methods and address limitations in various machine learning and AI applications. Current efforts focus on enhancing model performance and robustness through techniques like active learning, diffusion models, and transformer architectures, often incorporating advanced concepts such as graph isomorphism networks and attention mechanisms. These advancements have significant implications for various domains, including robotics, personalized recommendations, medical image analysis, and cybersecurity, by improving accuracy, efficiency, and interpretability. The overall goal is to create more powerful, reliable, and explainable AI systems.
Papers
The Master-Slave Encoder Model for Improving Patent Text Summarization: A New Approach to Combining Specifications and Claims
Shu Zhou, Xin Wang, Zhengda Zhou, Haohan Yi, Xuhui Zheng, Hao Wan
Transforming Engineering Diagrams: A Novel Approach for P&ID Digitization using Transformers
Jan Marius Stürmer, Marius Graumann, Tobias Koch
Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Ultrasound Imaging
Yuan Bi, Lucie Huang, Ricarda Clarenbach, Reza Ghotbi, Angelos Karlas, Nassir Navab, Zhongliang Jiang
Assessing and Enhancing Graph Neural Networks for Combinatorial Optimization: Novel Approaches and Application in Maximum Independent Set Problems
Chenchuhui Hu
Motion Graph Unleashed: A Novel Approach to Video Prediction
Yiqi Zhong, Luming Liang, Bohan Tang, Ilya Zharkov, Ulrich Neumann
Saliency-Based diversity and fairness Metric and FaceKeepOriginalAugment: A Novel Approach for Enhancing Fairness and Diversity
Teerath Kumar, Alessandra Mileo, Malika Bendechache
End-to-End Transformer-based Automatic Speech Recognition for Northern Kurdish: A Pioneering Approach
Abdulhady Abas Abdullah, Shima Tabibian, Hadi Veisi, Aso Mahmudi, Tarik Rashid
A Novel Approach to Grasping Control of Soft Robotic Grippers based on Digital Twin
Tianyi Xiang, Borui Li, Quan Zhang, Mark Leach, Eng Gee Lim