Artificial intelligence (AI) tools are increasingly being applied in drug discovery. While some
protagonists point to vast opportunities potentially offered by such tools, others remain …

While the choice of prior is one of the most critical parts of the Bayesian inference workflow,
recent Bayesian deep learning models have often fallen back on vague priors, such as …

Missing data in time series is a pervasive problem that puts obstacles in the way of
advanced analysis. A popular solution is imputation, where the fundamental challenge is to …

Multivariate time series with missing values are common in areas such as healthcare and
finance, and have grown in number and complexity over the years. This raises the question …

Spatially resolved transcriptomics (SRT) technologies have significantly advanced
biomedical research, but their data analysis remains challenging due to the discrete nature …

The recent evolution of generative artificial intelligence (GAI) leads to the emergence of
groundbreaking applications such as ChatGPT, which not only enhances the efficiency of …

Abstract We present Ordinary Differential Equation Variational Auto-Encoder (ODE2VAE), a
latent second order ODE model for high-dimensional sequential data. Leveraging the …

Efforts devoted to mitigate the effects of road traffic congestion have been conducted since
1970s. Nowadays, there is a need for prominent solutions capable of mining information …

Integrating physics models within machine learning models holds considerable promise
toward learning robust models with improved interpretability and abilities to extrapolate. In …

Factor analysis is a widely used method for dimensionality reduction in genome biology,
with applications from personalized health to single-cell biology. Existing factor analysis …