Outputs (3)

Passive Monitoring of Parkinson Tremor in Daily Life: A Prototypical Network Approach (2025)
Journal Article
Evers, L. J. W., Raykov, Y. P., Heskes, T. M., Krijthe, J. H., Bloem, B. R., & Little, M. A. (2025). Passive Monitoring of Parkinson Tremor in Daily Life: A Prototypical Network Approach. Sensors, 25(2), Article 366. https://doi.org/10.3390/s25020366

Objective and continuous monitoring of Parkinson’s disease (PD) tremor in free-living conditions could benefit both individual patient care and clinical trials, by overcoming the snapshot nature of clinical assessments. To enable robust detection of... Read More about Passive Monitoring of Parkinson Tremor in Daily Life: A Prototypical Network Approach.

Adaptive Latent Feature Sharing for Piecewise Linear Dimensionality Reduction (2024)
Journal Article
Farooq, A., Raykov, Y., Raykov, P., & Little, M. (2024). Adaptive Latent Feature Sharing for Piecewise Linear Dimensionality Reduction. Journal of Machine Learning Research, 25, Article 135

Linear Gaussian exploratory tools such as principal component analysis (PCA) and factor analysis (FA) are widely used for exploratory analysis, pre-processing, data visualization, and related tasks. Because the linear-Gaussian assumption is restricti... Read More about Adaptive Latent Feature Sharing for Piecewise Linear Dimensionality Reduction.

Principled Machine Learning (2022)
Journal Article
Raykov, Y. P., & Saad, D. (2022). Principled Machine Learning. IEEE Journal of Selected Topics in Quantum Electronics, 28(4), Article 0200419. https://doi.org/10.1109/JSTQE.2022.3186798

We introduce the underlying concepts which give rise to some of the commonly used machine learning methods, excluding deep-learning machines and neural networks. We point to their advantages, limitations and potential use in various areas of photonic... Read More about Principled Machine Learning.