A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Priyanka Kargupta, S. Li, Haocheng Wang, et al.
This paper synthesizes cognitive science into a taxonomy of 28 cognitive elements, evaluates 192K LLM traces across modalities, and develops test-time guidance improving performance by up to 66.7%.
Pallavi Sethi, Smruti R. Sarangi
A comprehensive survey of IoT architectures, protocols, and applications with a novel taxonomy covering technologies from sensors to applications.
Zahra Zamanzadeh Darban, Geoffrey I. Webb, Shirui Pan, et al.
A structured survey of deep learning methods for time series anomaly detection, including a taxonomy, advantages, limitations, and open challenges.
Philip Resnik
Proposes an information-based measure of semantic similarity in taxonomies that outperforms edge-counting and effectively resolves syntactic/semantic ambiguity.
Alysa Ziying Tan, Han Yu, Lizhen Cui, et al.
A survey of personalized federated learning techniques addressing data heterogeneity through a taxonomy of key challenges and strategies.
David Lewis, Yiming Yang, Tony Rose, et al.
This paper introduces RCV1, a large-scale benchmark of over 800,000 categorized newswire stories for text categorization, detailing its coding policy, taxonomy semantics, and corrections, while providing baseline results for supervised learning metho
Ranjan Sapkota, Konstantinos I. Roumeliotis, Manoj Karkee
This review provides a structured taxonomy distinguishing AI Agents from Agentic AI, analyzing their design philosophies, applications, and challenges.
Anil K. Jain, M. Narasimha Murty, Patrick J. Flynn
This paper provides a comprehensive overview of clustering methods from a statistical pattern recognition perspective, offering a taxonomy and practical guidance for practitioners.
Michael Wooldridge, Nicholas R. Jennings
This paper surveys the theory, architecture, and languages of intelligent agents, providing a foundational taxonomy for agent-based systems.
Wenxuan Wang, Zizhan Ma, Meidan Ding, et al.
First systematic review of LLM reasoning in medicine, proposing a taxonomy of training-time and test-time enhancement techniques across modalities and clinical applications.
Unknown
This paper systematically evaluates six training-free sparse attention methods for transformer LLMs, proposing a taxonomy along four design axes and deriving actionable insights.
Laura von Rueden, Sebastian Mayer, Katharina Beckh, et al.
This paper provides a taxonomy and survey of methods that integrate prior knowledge into machine learning to improve learning with limited data.