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
Machine learning approach to brain tumor detection and classification
Alice Oh, Inyoung Noh, Jian Choo, Jihoo Lee, Justin Park, Kate Hwang, Sanghyeon Kim, Soo Min Oh
Explainable Moral Values: a neuro-symbolic approach to value classification
Nicolas Lazzari, Stefano De Giorgis, Aldo Gangemi, Valentina Presutti
Learning to rumble: Automated elephant call classification, detection and endpointing using deep architectures
Christiaan M. Geldenhuys, Thomas R. Niesler
On Classification with Large Language Models in Cultural Analytics
David Bamman, Kent K. Chang, Li Lucy, Naitian Zhou
On the Training Convergence of Transformers for In-Context Classification
Wei Shen, Ruida Zhou, Jing Yang, Cong Shen
ECGN: A Cluster-Aware Approach to Graph Neural Networks for Imbalanced Classification
Bishal Thapaliya, Anh Nguyen, Yao Lu, Tian Xie, Igor Grudetskyi, Fudong Lin, Antonios Valkanas, Jingyu Liu, Deepayan Chakraborty, Bilel Fehri
Experiences from Creating a Benchmark for Sentiment Classification for Varieties of English
Dipankar Srirag, Jordan Painter, Aditya Joshi, Diptesh Kanojia
Use Random Selection for Now: Investigation of Few-Shot Selection Strategies in LLM-based Text Augmentation for Classification
Jan Cegin, Branislav Pecher, Jakub Simko, Ivan Srba, Maria Bielikova, Peter Brusilovsky
Towards Calibrated Losses for Adversarial Robust Reject Option Classification
Vrund Shah, Tejas Chaudhari, Naresh Manwani
Will LLMs Replace the Encoder-Only Models in Temporal Relation Classification?
Gabriel Roccabruna, Massimo Rizzoli, Giuseppe Riccardi
Optimizing Waste Management with Advanced Object Detection for Garbage Classification
Everest Z. Kuang, Kushal Raj Bhandari, Jianxi Gao
Lower-dimensional projections of cellular expression improves cell type classification from single-cell RNA sequencing
Muhammad Umar, Muhammad Asif, Arif Mahmood
Understanding Robustness of Parameter-Efficient Tuning for Image Classification
Jiacheng Ruan, Xian Gao, Suncheng Xiang, Mingye Xie, Ting Liu, Yuzhuo Fu
Fusion Based Hand Geometry Recognition Using Dempster-Shafer Theory
Asish Bera, Debotosh Bhattacharjee, Mita Nasipuri
Optimizing YOLO Architectures for Optimal Road Damage Detection and Classification: A Comparative Study from YOLOv7 to YOLOv10
Vung Pham, Lan Dong Thi Ngoc, Duy-Linh Bui
Time Traveling to Defend Against Adversarial Example Attacks in Image Classification
Anthony Etim, Jakub Szefer
A Comprehensive Survey and Classification of Evaluation Criteria for Trustworthy Artificial Intelligence
Louise McCormack, Malika Bendechache