Bayesian statistics is an approach to data analysis based on Bayes' theorem, where
available knowledge about parameters in a statistical model is updated with the information …

Modern deep learning methods constitute incredibly powerful tools to tackle a myriad of
challenging problems. However, since deep learning methods operate as black boxes, the …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Neural networks (NNs) are currently changing the computational paradigm on how to
combine data with mathematical laws in physics and engineering in a profound way …

To the Editor—Methods for analyzing single-cell data 1–4 perform a core set of
computational tasks. These tasks include dimensionality reduction, cell clustering, cell-state …

The posterior over Bayesian neural network (BNN) parameters is extremely high-
dimensional and non-convex. For computational reasons, researchers approximate this …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

Molecule representation learning (MRL) has been extensively studied and current methods
have shown promising power for various tasks, eg, molecular property prediction and target …

Diffusion models have emerged as a key pillar of foundation models in visual domains. One
of their critical applications is to universally solve different downstream inverse tasks via a …

How does language inform our downstream thinking? In particular, how do humans make
meaning from language--and how can we leverage a theory of linguistic meaning to build …