arXiv:2604.04552v1 Announce Type: cross
Abstract: Ensemble methods are widely used to improve predictive performance, but their effectiveness often comes at the cost of increased memory usage and computational complexity. In this paper, we identify a conflict in aggregation strategies that negatively impacts prediction stability. We propose StableTTA, a training-free method to improve aggregation stability and efficiency. Empirical results on ImageNet-1K show gains of 10.93–32.82% in top-1 accuracy, with 33 models achieving over 95% accuracy and several surpassing 96%. Notably, StableTTA allows lightweight architectures to outperform ViT by 11.75% in top-1 accuracy while using less than 5% of parameters and reducing computational cost by approximately 89.1% (in GFLOPs), enabling high-accuracy inference on resource-constrained devices.
Assessing nurses’ attitudes toward artificial intelligence in Kazakhstan: psychometric validation of a nine-item scale
BackgroundArtificial intelligence (AI) is increasingly integrated into healthcare, yet the attitudes and knowledge of nurses, who are the key mediators of AI implementation, remain underexplored.