A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Nicholas Berente, Bin Gu, Jan Recker, et al.
This editorial frames managing AI as decisions about autonomy, learning, and inscrutability at an evolving frontier, and introduces seven exemplar studies.
Wenya Linda Bi, Ahmed Hosny, Matthew B. Schabath, et al.
Review of AI applications in cancer imaging across lung, brain, breast, and prostate tumors, highlighting clinical challenges and the need for validation.
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Neutex introduces neural texture mapping for volumetric neural rendering, enabling realistic image synthesis by leveraging volumetric neural rendering techniques.
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AGIEval is a human-centric benchmark designed to evaluate foundation models on human-standardized exams and tasks, providing a more realistic assessment of their capabilities.
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This paper benchmarks tool-using agents under tool-environment unreliability, revealing their fragility and advancing evaluation along three dimensions.
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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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A survey of synthetic data generation methods for deep learning in computer vision and medical imaging, with emphasis on reducing data bias.
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This paper examines how foundation models are shifting biomedical imaging from task-specific models to unified backbones, and what it will take to turn the hype into clinical reality.
Plamen Angelov, Dimitar Filev
This paper introduces the Evolving Takagi-Sugeno (ETS) model, a recursive learning algorithm that dynamically updates fuzzy rule structure and parameters for online system identification.
Sarkar Siddique, James C. L. Chow
This topical review highlights how machine learning and AI applications in healthcare communication, including chatbots for COVID-19 education, cancer therapy, and medical imaging, can benefit humans.
Aurélie Pahud de Mortanges, Haozhe Luo, Shelley Zixin Shu, et al.
This paper reviews XAI for multimodal and longitudinal medical imaging data and proposes an XAI orchestrator to help clinicians synthesize data, predictions, and explanations.
Zhiyu Li, Shichao Song, Hanyu Wang, et al.
MemOS is a memory operating system for LLMs that elevates memory to a first-class resource, unifying parametric, activation, and plaintext memory via the MemCube abstraction for lifecycle management and cross-modal integration.