A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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Proposes efficient privacy-preserving machine learning for blockchain networks, addressing scalability and confidentiality challenges in decentralized applications.
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A survey of privacy-preserving machine learning techniques for e-health applications, covering methods, discussion, and future research directions.
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An empirical study showing that fine-tuned judge models are not a general substitute for GPT-4 in LLM evaluation, urging caution in real applications.
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A review of large language models like ChatGPT in medicine, covering their capabilities, applications, and challenges in clinical practice.
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A survey of large language models covering their development, capabilities, and applications across text generation, query response, and translation.
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The paper examines four prominent large language models used in education, including GPT-4 and LLaMA, and outlines their respective limitations.
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A tutorial surveying optimization techniques for multi-agent systems, covering challenges and solution methods across diverse multi-agent applications.
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A review of knowledge distillation and student-teacher learning for visual intelligence, summarizing methods, applications, and future directions.
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This paper reviews synthetic data as a solution to supply chain machine learning challenges, covering methods, applications, and research opportunities.
Qinggang Zhang, Shengyuan Chen, Yuanchen Bei, et al.
A systematic survey of Graph-based Retrieval-Augmented Generation (GraphRAG) for customizing large language models, covering techniques, applications, and future directions.
Hongyin Zhu
This paper systematically categorizes and answers practical questions about LLMs across five dimensions: computing power, software architecture, data, applications, and brain science.
Raouf Boutaba, Mohammad A. Salahuddin, Noura Limam, et al.
A comprehensive survey of machine learning applications across networking domains, covering techniques, challenges, and future research directions.