Artificial Intelligence Medical Compendium

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

Showing 45,771 to 45,780 of 224,055 articles

A Review of Uncertainty Representation and Quantification in Neural Networks.

IEEE transactions on pattern analysis and machine intelligence Mar 1, 2026
Effectively estimating the uncertainty attached to neural network predictions thus becomes essential to improve robustness, reliability, and trustworthiness. This paper provides an overview of various methodologies for representing, quantifying, and ... read more 

A Gravity-Informed Spatiotemporal Transformer for Human Activity Intensity Prediction.

IEEE transactions on pattern analysis and machine intelligence Mar 1, 2026
Human activity intensity prediction is crucial to many location-based services. Despite tremendous progress in modeling dynamics of human activity, most existing methods overlook physical constraints of spatial interaction, leading to uninterpretable... read more 

Generative Fuzzy System for Sequence-to-Sequence Learning via Rule-Based Inference.

IEEE transactions on neural networks and learning systems Mar 1, 2026
Generative models (GMs), particularly large language models (LLMs), have garnered significant attention in machine learning and artificial intelligence for their ability to generate new data by learning the statistical properties of training data and... read more 

HomLLM: Exploiting Semantic Homology Relationship for Fine-Grained Bird Image Classification via Large Language Models.

IEEE transactions on neural networks and learning systems Mar 1, 2026
How to recognize endangered bird species in complex outdoor environments has attracted considerable attention in the fields of computer vision and machine learning. However, fine-grained bird image classification (FBIC) is susceptible to problems suc... read more 

Progressive Feedforward Collapse of ResNet Training.

IEEE transactions on neural networks and learning systems Mar 1, 2026
Neural collapse (NC) is a simple and symmetric phenomenon for deep neural networks (DNNs) at the terminal phase of training, where the last-layer features collapse to their class means and form a simplex equiangular tight frame (ETF) aligning with th... read more 

NVS-SQA: Exploring Self-Supervised Quality Representation Learning for Neurally Synthesized Scenes Without References.

IEEE transactions on pattern analysis and machine intelligence Mar 1, 2026
Neural View Synthesis (NVS), such as NeRF and 3D Gaussian Splatting, effectively creates photorealistic scenes from sparse viewpoints, typically evaluated by quality assessment methods like PSNR, SSIM, and LPIPS. However, these full-reference methods... read more 

Unified Design Method for Suboptimal Control of Nonlinear System With Multiple Constraints.

IEEE transactions on cybernetics Mar 1, 2026
This article proposes a unified suboptimal controller design method for unknown general nonlinear systems subject to multiple constraints, including state, input, and output constraints. All inequality constraints are transformed into equality constr... read more 

Meta-Learning-Based Surrogate Models for Efficient Hyperparameter Optimization.

IEEE transactions on pattern analysis and machine intelligence Mar 1, 2026
Sequential Model-Based Optimization (SMBO) is a highly effective strategy for hyperparameter search in machine learning. It utilizes a surrogate model that fits previous trials and approximates the hyperparameter response surface (performance). This ... read more 

A Survey of Graph Neural Networks in Real World: Imbalance, Noise, Privacy and OOD Challenges.

IEEE transactions on pattern analysis and machine intelligence Mar 1, 2026
Graph-structured data exhibits universality and widespread applicability across diverse domains, such as social network analysis, biochemistry, financial fraud detection, and network security. Significant strides have been made in leveraging Graph Ne... read more 

PID: A Parameter-Efficient Isolation Domain-Incremental Learning Framework for Signal Modulation Classification.

IEEE transactions on neural networks and learning systems Mar 1, 2026
Deep neural networks have achieved promising progress in signal modulation classification (SMC), playing an essential role in a variety of applications such as cognitive radio networks, cyber defense, and electronic surveillance. However, most existi... read more 
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