Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 23,941 to 23,950 of 217,366 articles

Game Theory Meets Statistical Physics: A Novel Deep Neural Networks Design.

IEEE transactions on cybernetics May 1, 2026
We introduce a novel deep graphical representation that integrates game theory (GT) principles with the laws of statistical physics (SP), enabling feature extraction and pattern classification within a unified learning framework. In our approach, neu... read more 

Physics-Embedded Networks: Improving Convergence and Precision of Physics-Informed Neural Networks for Real-Time Applications.

IEEE transactions on cybernetics May 1, 2026
This article introduces the physics-embedded neural network (PENN), an enhanced physics-informed neural network (PINN) architecture tailored for visual servoing applications of multirotors. Classical PINNs, while interpretable and data-efficient due ... read more 

Learning Deep Tree-Based Retriever for Efficient Recommendation: Theory and Method.

IEEE transactions on pattern analysis and machine intelligence May 1, 2026
With the advancement of deep learning, deep recommendation models have achieved remarkable improvements in recommendation accuracy. However, due to the large number of candidate items in practice and the high cost of preference computation, these met... read more 

Continuous Review and Timely Correction: Enhancing the Resistance to Noisy Labels via Self-Not-True and Class-Wise Distillation.

IEEE transactions on pattern analysis and machine intelligence May 1, 2026
Deep neural networks possess remarkable learning capabilities but are vulnerable to overfitting in the presence of mislabeled data. A well-known memorization effect causes networks to first fit clean samples and later memorize noisy labels. Although ... read more 

Test-Time Adaptation for Detecting Image Inpainting Forgeries.

IEEE transactions on cybernetics May 1, 2026
The rapid development of deep learning-based image inpainting poses serious challenges to image authenticity. As inpainting methods continue to evolve, the inpainted images exhibit extremely high visual fidelity, presenting recognition difficulties t... read more 

Resilient Cooperative Optimal Output Regulation Control for Nonlinear Multiagent Systems.

IEEE transactions on cybernetics May 1, 2026
This article addresses the resilient cooperative optimal output regulation (COOR) control problem for nonlinear strict-feedback multiagent systems (MASs) under denial-of-service (DoS) attacks. By constructing the resilient adaptive distributed observ... read more 

Revisiting Out-of-Distribution Detection in Real-Time Object Detection: From Benchmark Pitfalls to a New Mitigation Paradigm.

IEEE transactions on pattern analysis and machine intelligence May 1, 2026
Out-of-distribution (OoD) inputs pose a persistent challenge to deep learning models, often triggering overconfident predictions on non-target objects. While prior work has primarily focused on refining scoring functions and adjusting test-time thres... read more 

Lifelong Learning of Large Language Model Based Agents: A Roadmap.

IEEE transactions on pattern analysis and machine intelligence May 1, 2026
Lifelong learning, also known as continual or incremental learning, is a crucial component for advancing Artificial General Intelligence (AGI) by enabling systems to continuously adapt in dynamic environments. While large language models (LLMs) have ... read more 

Novel Switching Laws for Switched Nonlinear Time-Delay Systems and Applications to Neural Networks.

IEEE transactions on cybernetics May 1, 2026
This article addresses the switching law design problem for switched nonlinear time-delay systems (SNTDSs). The existing switching laws, such as dwell time, average dwell time (ADT), and mode-dependent ADT (MDADT), depict the switching frequency by l... read more 

GrowSP++: Growing Superpoints and Primitives for Unsupervised 3D Semantic Segmentation.

IEEE transactions on pattern analysis and machine intelligence May 1, 2026
We study the problem of 3D semantic segmentation from raw point clouds. Unlike existing methods which primarily rely on a large amount of human annotations for training neural networks, we proposes GrowSP++, an unsupervised method to successfully ide... read more 
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