
Neighborhood Contrastive Learning Applied to Online Patient Monitoring
Intensive care units (ICU) are increasingly looking towards machine lear...
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Boosting Variational Inference With Locally Adaptive StepSizes
Variational Inference makes a tradeoff between the capacity of the vari...
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Early prediction of respiratory failure in the intensive care unit
The development of respiratory failure is common among patients in inten...
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Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning
Marginallikelihood based modelselection, even though promising, is rar...
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Bayesian Neural Network Priors Revisited
Isotropic Gaussian priors are the de facto standard for modern Bayesian ...
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On Disentanglement in Gaussian Process Variational Autoencoders
Complex multivariate time series arise in many fields, ranging from comp...
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WRSE – a nonparametric weightedresolution ensemble for predicting individual survival distributions in the ICU
Dynamic assessment of mortality risk in the intensive care unit (ICU) ca...
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A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation
The idea behind the unsupervised learning of disentangled representation...
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Scalable Gaussian Process Variational Autoencoders
Conventional variational autoencoders fail in modeling correlations betw...
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A Commentary on the Unsupervised Learning of Disentangled Representations
The goal of the unsupervised learning of disentangled representations is...
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WeaklySupervised Disentanglement Without Compromises
Intelligent agents should be able to learn useful representations by obs...
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CommunicationEfficient Jaccard Similarity for HighPerformance Distributed Genome Comparisons
Jaccard Similarity index is an important measure of the overlap of two s...
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Variational PSOM: Deep Probabilistic Clustering with SelfOrganizing Maps
Generating visualizations and interpretations from highdimensional data...
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Deep Multiple Instance Learning for Taxonomic Classification of Metagenomic read sets
Metagenomic studies have increasingly utilized sequencing technologies i...
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Multivariate Time Series Imputation with Variational Autoencoders
Multivariate time series with missing values are common in many areas, f...
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Disentangling Factors of Variation Using Few Labels
Learning disentangled representations is considered a cornerstone proble...
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Unsupervised Extraction of Phenotypes from Cancer Clinical Notes for Association Studies
The recent adoption of Electronic Health Records (EHRs) by health care p...
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Machine learning for early prediction of circulatory failure in the intensive care unit
Intensive care clinicians are presented with large quantities of patient...
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Deep Mean Functions for MetaLearning in Gaussian Processes
Fitting machine learning models in the lowdata limit is challenging. Th...
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Scalable Gaussian Processes on Discrete Domains
Kernel methods on discrete domains have shown great promise for many cha...
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Deep SelfOrganization: Interpretable Discrete Representation Learning on Time Series
Human professionals are often required to make decisions based on comple...
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Boosting Black Box Variational Inference
Approximating a probability density in a tractable manner is a central t...
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Clustering Meets Implicit Generative Models
Clustering is a cornerstone of unsupervised learning which can be though...
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Revisiting FirstOrder Convex Optimization Over Linear Spaces
Two popular examples of firstorder optimization methods over linear spa...
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Boosting Variational Inference: an Optimization Perspective
Variational Inference is a popular technique to approximate a possibly i...
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Realvalued (Medical) Time Series Generation with Recurrent Conditional GANs
Generative Adversarial Networks (GANs) have shown remarkable success as ...
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Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees
Greedy optimization methods such as Matching Pursuit (MP) and FrankWolf...
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Learning Unitary Operators with Help From u(n)
A major challenge in the training of recurrent neural networks is the so...
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Knowledge Transfer with Medical Language Embeddings
Identifying relationships between concepts is a key aspect of scientific...
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A Generative Model of Words and Relationships from Multiple Sources
Neural language models are a powerful tool to embed words into semantic ...
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Framework for Multitask Multiple Kernel Learning and Applications in Genome Analysis
We present a general regularizationbased framework for Multitask learn...
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Bayesian representation learning with oracle constraints
Representation learning systems typically rely on massive amounts of lab...
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Automatic Relevance Determination For Deep Generative Models
A recurring problem when building probabilistic latent variable models i...
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Probabilistic Clustering of TimeEvolving Distance Data
We present a novel probabilistic clustering model for objects that are r...
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GRED: GraphRegularized 3D Shape Reconstruction from Highly Anisotropic and Noisy Images
Analysis of microscopy images can provide insight into many biological p...
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Gunnar Rätsch
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Full Professor/ Professor for Biomedical Informatics in the Computer Science Department at ETH Zürich in collaboration with the University Spital in Zürich since 2016, Associate Faculty Member in Computational Biology at Memorial SloanKettering Cancer Center since 2012, Associate Professor in Computational Biology at Weill Cornell Medical College since 2012, Group Leader at Friedrich Miescher Laboratory of the Max Planck Society from 20052012, Postdoctoral Researcher at Max Planck Institute for Biological Cybernetics 20032004, Postdoctoral Researcher at Fraunhofer Institute FIRST 20032004, Postdoc at Australian National University from 20012003, Research associate at UC Santa Cruz 2001, Research associate at GMD (now Fraunhofer) FIRST from 19972001