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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HT2ML combines homomorphic encryption and trusted execution environments to enable efficient privacy-preserving machine learning on outsourced cloud data.
Diana‐Margarita Córdova‐Esparza
A systematic review of 82 studies (2023–2025) on AI-powered educational agents, proposing design principles and safeguards, and finding hybrid human–AI workflows outperform fully autonomous tutors.
Mark V. Pauly, Richard Keiser, Leif Kobbelt, et al.
A hybrid geometry representation combining unstructured point clouds with moving least squares implicit surfaces enables versatile free-form shape modeling with boolean operations and topology control.
Li Deng
This tutorial survey provides a comprehensive overview of deep learning architectures, algorithms, and applications, categorizing them into generative, discriminative, and hybrid classes.
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This paper categorizes self-supervised learning models into generative, contrastive, and hybrid approaches, providing a taxonomy and analysis of their underlying principles.
Marina Yusoff, Toto Haryanto, Heru Suhartanto, et al.
This structured review analyzes deep learning methods, especially CNNs and hybrids, for histopathological breast cancer image classification, highlighting challenges like overfitting and data imbalance.
R. Rashmi, V. Upadhya
MAHA is a novel multimodal RAG architecture that uses modality-aware knowledge graphs and hybrid retrieval to improve unstructured data retrieval.
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Show-1 introduces a hybrid text-to-video generation framework that combines pixel-based and latent diffusion models to leverage their complementary strengths.
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Introduces Large Hybrid-Reasoning Models that adaptively decide when to think based on query context, balancing efficiency and reasoning quality.
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Nemotron-flash introduces a hybrid small language model architecture that optimizes latency for efficient deployment in real-world applications.
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This paper explores the integration of soft computing techniques, particularly neuro-fuzzy systems, into AI agents to enhance performance through hybrid collaborative approaches.
Yue‐Jiao Gong, Jingjing Li, Yicong Zhou, et al.
Proposes GL-PSO, a hybrid PSO-GA algorithm that uses genetic evolution to breed high-quality exemplars for particle swarm optimization, enhancing global search and efficiency.