1 citations · 2 across the 7 of their papers we have counts for
7 papers
Investigating the Influence of Prompt and Response Languages on LLM Content Generation
Thi Thanh Nhan Nguyen, Mai Khoi Tieu, Michael A. Riegler +2
This study examines how prompt and response language influence the behavior of large language models. Using five models, we evaluated answers to 68 non translation questions across…
Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization
Hugo L. Hammer, Vajira Thambawita, Kristoffer Herland Hellton +1
Interpretable Mesomorphic Neural Networks (IMNs) offer a promising framework that combines the predictive power of deep neural networks with the interpretability of linear models.…
Sampling Matters: The Effect of ECG Frequency on Deep Learning-Based Atrial Fibrillation Detection
Arjan Mahmuod, Adrian Rod Hammerstad, Muzaffar Yousef +5
Deep learning models for atrial fibrillation (AF) detection are increasingly trained on heterogeneous electrocardiogram (ECG) datasets with varying sampling frequencies, yet the sp…
Medical Imaging AI Competitions Lack Fairness
Annika Reinke, Evangelia Christodoulou, Sthuthi Sadananda +34
Benchmarking competitions are central to the development of artificial intelligence (AI) in medical imaging, defining performance standards and shaping methodological progress. How…
Energy-Conscious LLM Decoding: Impact of Text Generation Strategies on GPU Energy Consumption
Alireza Nik, Michael A. Riegler, Pål Halvorsen
Decoding strategies significantly influence the quality and diversity of the generated text in Large Language Models (LLMs), yet their impact on computational resources, particular…
DPERC: Direct Parameter Estimation for Mixed Data
Tuan L. Vo, Quan Huu Do, Uyen Dang +4
The covariance matrix is a foundation in numerous statistical and machine-learning applications such as Principle Component Analysis, Correlation Heatmap, etc. However, missing val…
Explainability of Machine Learning Models under Missing Data
Tuan L. Vo, Thu Nguyen, Luis M. Lopez-Ramos +3
Missing data is a prevalent issue that can significantly impair model performance and explainability. This paper briefly summarizes the development of the field of missing data wit…