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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A survey of privacy-preserving machine learning techniques for e-health applications, covering methods, discussion, and future research directions.
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This paper examines how large language models are transforming scientific research and discusses the opportunities and challenges they present for the scientific community.
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This paper reviews synthetic data as a solution to supply chain machine learning challenges, covering methods, applications, and research opportunities.
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Genie 3 is presented as a significant advance in world models, poised to impact AI research and generative media.
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This paper examines how to benchmark biomedical foundation models and questions their epistemological value in biomedical research.
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A comprehensive review of foundation models in robotics, covering methods, models, datasets, challenges, and future research directions.
Raymond Kosala, Hendrik Blockeel
This survey organizes Web mining research into three categories—content, structure, and usage mining—and connects them to the agent paradigm.
Shriniket Dixit, Anant Kumar, Kathiravan Srinivasan, et al.
This review explores AI's contributions to improving CRISPR-based genome editing, addressing challenges like off-target effects and delivery, and discussing future research directions.
Nathan A. Baker, Frank Alexander, T. Bremer, et al.
This workshop report outlines core technologies and basic research needs for scientific machine learning and AI, emphasizing a national strategy for advancing the field.
De Jong Yeong, Gustavo Velasco-Hernandez, John M. Barry, et al.
This paper provides an end-to-end review of sensor hardware, calibration, and fusion techniques for object detection in autonomous vehicles, highlighting challenges and future research directions.
Junfei Qiu, Qihui Wu, Guoru Ding, et al.
This survey reviews machine learning techniques for big data processing, highlighting promising methods and discussing challenges, connections to signal processing, and open research trends.
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.