Bayesianize is a lightweight Bayesian neural network (BNN) wrapper in pytorch. The overall goal is to allow for easy conversion of neural networks in existing scripts to BNNs with minimal changes to ...

We propose an approach for joint trajectory analysis of multiple single-cell sequencing data, combining Bayesian hierarchical models with variational autoencoders. Based on a coherent statistical ...

Abstract: In this paper, a Variational Autoencoder (VAE) based framework is introduced to solve parameter estimation problems for non-linear forward models. In particular, we focus on applications in ...

Abstract: Hyperspectral images (HSI) are corrupted by a combination of Gaussian and impulse noise. Successful denoising of HSI data increases the accuracy of high-level vision operations like ...

Pure-tone audiometry—the process of estimating a person's hearing threshold from “audible” and “inaudible” responses to tones of varying frequency and intensity—is the basis for diagnosing and ...

Background Maternal mortality remains a public health problem despite several global efforts. Globally, about 830 women die of pregnancy-related death per day, with more than two-third of these cases ...

Many domains of science have developed complex simulations to describe phenomena of interest. While these simulations provide high-fidelity models, they are poorly suited for inference and lead to ...

Institute for Atmospheric and Climate Science, ETH Zurich, Zurich 8092, Switzerland Institute of Biogeochemistry and Pollutant Dynamics, ETH Zurich, Zurich 8092, Switzerland ...

Although classical decision-making studies have assumed that subjects behave in a Bayes-optimal way, the sub-optimality that causes biases in decision-making is currently under debate. Here, we ...

In this blog post, I am going to teach you how to train a Bayesian deep learning classifier using Keras and tensorflow. Before diving into the specific training example, I will cover a few important ...