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
Dynamic neuronal networks efficiently achieve classification in robotic interactions with real-world objects
Pakorn Uttayopas, Xiaoxiao Cheng, Udaya Bhaskar Rongala, Henrik Jörntell, Etienne Burdet
Classification by estimating the cumulative distribution function for small data
Meng-Xian Zhu, Yuan-Hai Shao
Polar Encoding: A Simple Baseline Approach for Classification with Missing Values
Oliver Urs Lenz, Daniel Peralta, Chris Cornelis
Cross-Geography Generalization of Machine Learning Methods for Classification of Flooded Regions in Aerial Images
Sushant Lenka, Pratyush Kerhalkar, Pranav Shetty, Harsh Gupta, Bhavam Vidyarthi, Ujjwal Verma