Papers for

biomedical data scientists

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Efficient neural network design improves subject-based predictions

Efficient Leakage-Free Neural Architecture Search under Leave-One-Subject-Out Evaluation

Abstract: Leave-One-Subject-Out (LOSO) evaluation estimates generalisation performance for subject-based classification but makes Neural Architecture Search (NAS) computationally expensive because a fully nested implementation requires N independent architecture searches and, assuming approximately linear training cost, scales as O(N^2). We propose a leakage-free, block-based approach that shares NAS runs across subjects. On the BioVid Heat Pain dataset, our approach increased the mean accuracy from 82.79% to 83.39% while reducing the number of parameters by up to 99.2%.

Tue 8 SeptMachine LearningArtificial Intelligence

The gist

Predicting information for one person when you have data from many others can be slow and costly when designing neural networks. The authors developed a method to speed this up by sharing parts of the design process between people, avoiding mistakes that mix training and testing data. Their method improved accuracy slightly and greatly reduced the size of the networks tested on a pain detection dataset.

Open → 2609.09433v1