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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This survey provides a comprehensive overview of generative diffusion models, covering their theoretical foundations, key methodologies, and applications in computer vision.
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This survey provides a comprehensive overview of neural network-based uncertainty quantification methodologies and their applications in decision making.
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This paper systematically reviews evaluation methodologies for agentic AI systems, arguing that progress toward trustworthy agentic AI depends on evolving evaluation approaches.
Nourhan Ibrahim, Samar AboulEla, Ahmed Ibrahim, et al.
This survey classifies LLM-KG integration into three paradigms—KG-augmented LLMs, LLM-augmented KGs, and synergized frameworks—and evaluates their methodologies, metrics, benchmarks, and challenges.
Yibo Yan, Jiamin Su, Jianxiang He, et al.
First comprehensive survey of mathematical reasoning in multimodal LLMs, reviewing over 200 studies since 2021 across benchmarks, methodologies, and challenges.
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This survey provides a comprehensive overview of parameter-efficient fine-tuning (PEFT) methodologies for large language models, addressing the computational challenges of full fine-tuning.
Akash Ghosh, Arkadeep Acharya, Sriparna Saha, et al.
This survey systematically classifies Vision-Language Models (VLMs) based on their input modalities and provides a comprehensive taxonomy of current methodologies and future directions.
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This survey reviews reasoning capabilities in foundation models, covering concepts, methodologies, and future outlook.
Somnath Mazumdar, Daniel Seybold, Kyriakos Kritikos, et al.
A survey of cloud-centric Big Data placement and storage methodologies, focusing on non-functional properties to improve data management cost and application performance.