Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2023; 32nd International C Lazaros Iliadis,Antonios Papaleonidas,Chrisina Jay Confe

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,Adaptive Embedding and?Distribution Re-margin for?Long-Tail Recognition,ype, calibrated with the knowledge from well-represented head classes and adaptive embedding uncertainty estimation. We conduct extensive experiments on a common long-tailed benchmark CIFAR100-LT. Experimental results demonstrate the advantage of the proposed AMDRG for the long-tailed recognition pr

Leaven

Adaptive Propagation Network Based on Multi-scale Information Fusion, Experimental results show that MAPNET partially solves the over-smoothing problem in GNNs. The model outperforms recent models in both semi-supervised and fully supervised node classification tasks on multiple datasets, and has better generalization performance and robustness.

Mumble

卜聞

Jogging

圓木可阻礙

Can Machine Learning Support Improvement in Effective Nutrition of Patients in Critical Care Units?port enhanced patient care. The research has been extended to a different area of interest, this time focusing on paediatric CCU patients. The aim here it to use similar research methodologies to attempt to estimate energy expenditure for very young patients. This bears the challenge of having to us

Contracture

,Cross-Domain Transformer with?Adaptive Thresholding for?Domain Adaptive Semantic Segmentation,ynamically adjusts the proportion of pseudo-labels that are used in training based on the model’s adaptation status. Our approach guarantees the reliability of the pseudo labels while allowing more target domain samples to contribute to model training. Extensive experiments show that our proposed me

忍耐

Delineation of Prostate Boundary from Medical Images via a Mathematical Formula-Based Hybrid Algoriutilized to construct an interpretable and smooth mathematical equation of the organ border. The Dice similarity coefficient (DSC), Jaccard similarity coefficient (OMG), and accuracy (ACC) of model outputs against ground-truths were 95.9 ± 2.3%, 94.9 ± 2.4%, and 95.3 ± 2.2%, respectively. The result

DEFER

Benign