Transformer Based Approach
Transformer-based approaches are revolutionizing various fields by leveraging attention mechanisms to process sequential and structured data more effectively than traditional methods. Current research focuses on adapting transformer architectures, such as those inspired by DETR and incorporating techniques like graph convolutional transformers, to diverse applications including anomaly detection, image processing, natural language processing, and time series forecasting. This versatility significantly impacts numerous scientific domains and practical applications, offering improvements in accuracy, efficiency, and the ability to handle complex relationships within data.
65papers
Papers
March 4, 2025
Learning from Noisy Labels with Contrastive Co-Transformer
Yan Han, Soumava Kumar Roy, Mehrtash Harandi, Lars PeterssonAustralian National University●Monash University●CSIROExamining the Mental Health Impact of Misinformation on Social Media Using a Hybrid Transformer-Based Approach
Sarvesh Arora, Sarthak Arora, Deepika Kumar, Vallari Agrawal, Vedika Gupta, Dipit VasdevBharati Vidyapeeth’s College of Engineering●Jindal Global Business School●New York University
December 25, 2024
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