The rapid advancement of artificial intelligence (AI) offers exciting opportunities for
marketing practice and academic research. In this study, through the application of natural …

The rapid and enthusiastic adoption of single-cell RNA sequencing (scRNA-seq) has
demonstrated that this technology is far more than just another way to perform transcriptome …

Interpretability in machine learning (ML) is crucial for high stakes decisions and
troubleshooting. In this work, we provide fundamental principles for interpretable ML, and …

Recent studies have revealed that deep neural networks (DNNs) are vulnerable to backdoor
attacks, where attackers embed hidden backdoors in the DNN model by poisoning a few …

Federated learning has evolved to improve a single global model under data heterogeneity
(as a curse) or to develop multiple personalized models using data heterogeneity (as a …

Single-cell transcriptomics yields ever growing data sets containing RNA expression levels
for thousands of genes from up to millions of cells. Common data analysis pipelines include …

Accurate and comprehensive extraction of information from high-dimensional single cell
datasets necessitates faithful visualizations to assess biological populations. A state-of-the …

Abstract Semi-Supervised Domain Adaptation (SSDA) involves learning to classify unseen
target data with a few labeled and lots of unlabeled target data, along with many labeled …

Ionotropic glutamate receptors (iGluRs) are tetrameric ligand-gated ion channels that open
their pores in response to binding of the agonist glutamate,–. An ionic current through a …

Uncertainty awareness is crucial to develop reliable machine learning models. In this work,
we propose the Natural Posterior Network (NatPN) for fast and high-quality uncertainty …