A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Felix Gremse, Marius Stärk, Josef Ehling, et al.
A GPU-accelerated software tool for interactive segmentation and rendering of multimodal biomedical volume data.
L. McCowan, Daniel Gática-Pérez, Samy Bengio, et al.
This paper proposes HMM-based models that capture interactions between participants from audiovisual features to recognize group actions in meetings.
Zongxi Li, Zijian Wang, Weiming Wang, et al.
A systematic survey of Retrieval-Augmented Generation (RAG) in education, covering workflow, retrievers, generation optimization, and applications.
Erlan Yu, Xuehong Chu, Wanwan Zhang, et al.
This review provides a comprehensive overview of LLM applications, challenges, and future directions in medicine, highlighting hallucination, interpretability, and ethical concerns.
Xuedong Huang, Alex Acero, Hsiao-Wuen Hon, et al.
A comprehensive guide to spoken language processing covering theory, algorithms, and system development from speech recognition to synthesis and understanding.
Bjoern Menze, András Jakab, Stefan Bauer, et al.
The BRATS benchmark established a standardized evaluation framework for brain tumor segmentation, revealing that no single algorithm outperforms all sub-regions but fusing multiple methods via majority vote surpasses individual performance.
Zabir Al Nazi, Wei Peng
A comprehensive survey of LLMs in healthcare, covering their development from PLMs to current state, applications, performance metrics, 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.
Wenxuan Wang, Zizhan Ma, Meidan Ding, et al.
First systematic review of LLM reasoning in medicine, proposing a taxonomy of training-time and test-time enhancement techniques across modalities and clinical applications.
Weigao Sun, Jiaxi Hu, Yucheng Zhou, et al.
A systematic survey of efficient LLM architectures addressing transformer limitations, covering linear/sparse models, efficient attention, MoE, hybrids, and diffusion LLMs.
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CoSyn uses LLMs to generate code that renders synthetic images, then uses that code as context to produce high-quality instruction-tuning data for vision-language models.
Yupan Huang, Tengchao Lv, Lei Cui, et al.
LayoutLMv3 introduces a unified text-image multimodal Transformer with masked language and image modeling for document AI.