A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
J. Makhoul
A tutorial review of linear prediction for discrete signals, covering all-pole and pole-zero models, least squares estimation, spectral matching, and applications to data compression.
Unknown
A tutorial surveying optimization techniques for multi-agent systems, covering challenges and solution methods across diverse multi-agent applications.
Giorgio Roffo
This tutorial explores advanced LLM techniques including RAG, PAL, ReAct, LangChain, fine-tuning strategies, and transformer architectures to address limitations like temporal cutoffs and mathematical inaccuracies.
Li Deng
This tutorial survey provides a comprehensive overview of deep learning architectures, algorithms, and applications, categorizing them into generative, discriminative, and hybrid classes.
Unknown
This paper provides a comprehensive tutorial, review, and perspective on offline reinforcement learning, outlining key challenges and open problems.
Unknown
This paper provides a comprehensive tutorial on the principles of diffusion models, building from foundational concepts to advanced developments for readers seeking deeper understanding.
Luca Benini, Giovanni De Micheli
This tutorial surveys system-level design methods for energy-efficient electronic systems, covering hardware and software energy reduction across design phases.
Prakash M. Nadkarni, Lucila Ohno‐Machado, Wendy W. Chapman
This tutorial provides an overview of natural language processing, its historical evolution, common sub-problems, medical applications, machine-learning approaches, and modern system architectures.
Alexey Natekin, Alois Knoll
This tutorial provides a comprehensive introduction to gradient boosting machines, covering methodology, model complexity, and practical applications.
Grégoire Montavon, Wojciech Samek, Klaus‐Robert Müller
This tutorial paper introduces deep neural network interpretability, focusing on layer-wise relevance propagation (LRP) for explaining predictions.
Hugo Laurençon, Andrés Marafioti, Victor Sanh, et al.
This paper provides a comprehensive tutorial on building vision-language models, focusing on architecture, data, and training methods.
Ke-Lin Du, Bingchun Jiang, Jiabin Lu, et al.
A comprehensive survey and tutorial on kernel machines and SVMs, covering principles, training methods, and future directions.