With an increase in deep learning-based methods, the call for explainability of such methods
grows, especially in high-stakes decision making areas such as medical image analysis …

Deep generative models are a class of techniques that train deep neural networks to model
the distribution of training samples. Research has fragmented into various interconnected …

Diffusion models have shown incredible capabilities as generative models; indeed, they
power the current state-of-the-art models on text-conditioned image generation such as …

Score-based generative models (SGMs) have recently demonstrated impressive results in
terms of both sample quality and distribution coverage. However, they are usually applied …

To the Editor—Methods for analyzing single-cell data 1, 2, 3, 4 perform a core set of
computational tasks. These tasks include dimensionality reduction, cell clustering, cell-state …

Normalizing flows, autoregressive models, variational autoencoders (VAEs), and deep
energy-based models are among competing likelihood-based frameworks for deep …

The paired measurement of RNA and surface proteins in single cells with cellular indexing
of transcriptomes and epitopes by sequencing (CITE-seq) is a promising approach to …

Despite recent advances in metagenomic binning, reconstruction of microbial species from
metagenomics data remains challenging. Here we develop variational autoencoders for …

An Introduction to Variational Autoencoders Page 1 An Introduction to Variational Autoencoders
Page 2 Other titles in Foundations and Trends R in Machine Learning Computational Optimal …

Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …