Classification Code
Classification code research focuses on developing and improving algorithms and models to accurately assign data points to predefined categories. Current efforts concentrate on addressing challenges like imbalanced datasets, noisy data, and limited labeled data through techniques such as self-supervised pre-training, robust loss functions, and the application of diverse architectures including convolutional neural networks (CNNs), transformers, and novel approaches like Mamba. These advancements have significant implications across various fields, improving accuracy and efficiency in applications ranging from medical image analysis and bioacoustic monitoring to cybersecurity threat detection and scientific literature organization.
845papers
Papers - Page 20
October 3, 2024
September 30, 2024
Text Clustering as Classification with LLMs
Classification of Radiological Text in Small and Imbalanced Datasets in a Non-English Language
Whole-Graph Representation Learning For the Classification of Signed Networks
Classification with a Network of Partially Informative Agents: Enabling Wise Crowds from Individually Myopic Classifiers
September 27, 2024
September 25, 2024
Targeted Neural Architectures in Multi-Objective Frameworks for Complete Glioma Characterization from Multimodal MRI
Classification of Gleason Grading in Prostate Cancer Histopathology Images Using Deep Learning Techniques: YOLO, Vision Transformers, and Vision Mamba
SSP-RACL: Classification of Noisy Fundus Images with Self-Supervised Pretraining and Robust Adaptive Credal Loss
September 24, 2024
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September 15, 2024