97 citations · 272 across the 14 of their papers we have counts for
14 papers
Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing Conditional Mutual Information
Fedor Sergeev, Paola Malsot, Gunnar Rätsch +1
Knowing which features of a multivariate time series to measure and when is a key task in medicine, wearables, and robotics. Better acquisition policies can reduce costs while main…
Early prediction of respiratory failure in the intensive care unit
Matthias Hüser, Martin Faltys, Xinrui Lyu +4
The development of respiratory failure is common among patients in intensive care units (ICU). Large data quantities from ICU patient monitoring systems make timely and comprehensi…
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning
Alexander Immer, Matthias Bauer, Vincent Fortuin +2
Marginal-likelihood based model-selection, even though promising, is rarely used in deep learning due to estimation difficulties. Instead, most approaches rely on validation data,…
On Disentanglement in Gaussian Process Variational Autoencoders
Simon Bing, Vincent Fortuin, Gunnar Rätsch
Complex multivariate time series arise in many fields, ranging from computer vision to robotics or medicine. Often we are interested in the independent underlying factors that give…
WRSE -- a non-parametric weighted-resolution ensemble for predicting individual survival distributions in the ICU
Jonathan Heitz, Joanna Ficek, Martin Faltys +3
Dynamic assessment of mortality risk in the intensive care unit (ICU) can be used to stratify patients, inform about treatment effectiveness or serve as part of an early-warning sy…
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation
Francesco Locatello, Stefan Bauer, Mario Lucic +4
The idea behind the \emph{unsupervised} learning of \emph{disentangled} representations is that real-world data is generated by a few explanatory factors of variation which can be…
Scalable Gaussian Process Variational Autoencoders
Metod Jazbec, Matthew Ashman, Vincent Fortuin +3
Conventional variational autoencoders fail in modeling correlations between data points due to their use of factorized priors. Amortized Gaussian process inference through GP-VAEs…
Weakly-Supervised Disentanglement Without Compromises
Francesco Locatello, Ben Poole, Gunnar Rätsch +3
Intelligent agents should be able to learn useful representations by observing changes in their environment. We model such observations as pairs of non-i.i.d. images sharing at lea…
META: Memory-efficient taxonomic classification and abundance estimation for metagenomics with deep learning
Andreas Georgiou, Vincent Fortuin, Harun Mustafa +1
Metagenomic studies have increasingly utilized sequencing technologies in order to analyze DNA fragments found in environmental samples.One important step in this analysis is the t…
GP-VAE: Deep Probabilistic Time Series Imputation
Vincent Fortuin, Dmitry Baranchuk, Gunnar Rätsch +1
Multivariate time series with missing values are common in areas such as healthcare and finance, and have grown in number and complexity over the years. This raises the question wh…
Meta-Learning Mean Functions for Gaussian Processes
Vincent Fortuin, Heiko Strathmann, Gunnar Rätsch
When fitting Bayesian machine learning models on scarce data, the main challenge is to obtain suitable prior knowledge and encode it into the model. Recent advances in meta-learnin…
Bayesian representation learning with oracle constraints
Theofanis Karaletsos, Serge Belongie, Gunnar Rätsch
Representation learning systems typically rely on massive amounts of labeled data in order to be trained to high accuracy. Recently, high-dimensional parametric models like neural…
Automatic Relevance Determination For Deep Generative Models
Theofanis Karaletsos, Gunnar Rätsch
A recurring problem when building probabilistic latent variable models is regularization and model selection, for instance, the choice of the dimensionality of the latent space. In…
DNA methylation variation in Arabidopsis has a genetic basis and shows evidence of local adaptation
Manu J. Dubin, Pei Zhang, Dazhe Meng +15
Epigenome modulation in response to the environment potentially provides a mechanism for organisms to adapt, both within and between generations. However, neither the extent to whi…