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.
Papers
Morphological Detection and Classification of Microplastics and Nanoplastics Emerged from Consumer Products by Deep Learning
Hadi Rezvani, Navid Zarrabi, Ishaan Mehta, Christopher Kolios, Hussein Ali Jaafar, Cheng-Hao Kao, Sajad Saeedi, Nariman Yousefi
Classification of Buried Objects from Ground Penetrating Radar Images by using Second Order Deep Learning Models
Douba Jafuno, Ammar Mian, Guillaume Ginolhac, Nickolas Stelzenmuller
DomURLs_BERT: Pre-trained BERT-based Model for Malicious Domains and URLs Detection and Classification
Abdelkader El Mahdaouy, Salima Lamsiyah, Meryem Janati Idrissi, Hamza Alami, Zakaria Yartaoui, Ismail Berrada
Pushing Joint Image Denoising and Classification to the Edge
Thomas C Markhorst, Jan C van Gemert, Osman S Kayhan
Landscape-Aware Automated Algorithm Configuration using Multi-output Mixed Regression and Classification
Fu Xing Long, Moritz Frenzel, Peter Krause, Markus Gitterle, Thomas Bäck, Niki van Stein
CoLaNET -- A Spiking Neural Network with Columnar Layered Architecture for Classification
Mikhail Kiselev
EnsLoss: Stochastic Calibrated Loss Ensembles for Preventing Overfitting in Classification
Ben Dai