This survey presents multidimensional scaling (MDS) methods and their applications in real
world. MDS is an exploratory and multivariate data analysis technique becoming more and …

Current and future wireless applications strongly rely on precise real-time localization. A
number of applications, such as smart cities, Internet of Things (IoT), medical services …

Abstract Single-cell RNA-Seq (scRNA-Seq) profiles gene expression of individual cells.
Recent scRNA-Seq datasets have incorporated unique molecular identifiers (UMIs). Using …

Topic models are one of the most popular methods for learning representations of text, but a
major challenge is that any change to the topic model requires mathematically deriving a …

Causal inference from observational data is a vital problem, but it comes with strong
assumptions. Most methods assume that we observe all confounders, variables that affect …

Linear dimensionality reduction methods are a cornerstone of analyzing high dimensional
data, due to their simple geometric interpretations and typically attractive computational …

We develop stochastic variational inference, a scalable algorithm for approximating
posterior distributions. We develop this technique for a large class of probabilistic models …

Traditional relation extraction predicts relations within some fixed and finite target schema.
Machine learning approaches to this task require either manual annotation or, in the case of …

Principal components analysis (PCA) is a well-known technique for approximating a tabular
data set by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets …

Understanding any brain circuit will require a categorization of its constituent neurons. In
hippocampal area CA1, at least 23 classes of GABAergic neuron have been proposed to …