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07/10/2023
Multi-modal Graph Learning over UMLS Knowledge Graphs
Clinicians are increasingly looking towards machine learning to gain ins...
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06/06/2023
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels
Selecting hyperparameters in deep learning greatly impacts its effective...
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06/01/2023
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding
A prominent challenge of offline reinforcement learning (RL) is the issu...
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05/26/2023
Laplace-Approximated Neural Additive Models: Improving Interpretability with Bayesian Inference
Deep neural networks (DNNs) have found successful applications in many f...
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12/06/2022
On the Importance of Clinical Notes in Multi-modal Learning for EHR Data
Understanding deep learning model behavior is critical to accepting mach...
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08/29/2022
Temporal Label Smoothing for Early Prediction of Adverse Events
Models that can predict adverse events ahead of time with low false-alar...
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02/26/2022
Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization
We propose a stochastic conditional gradient method (CGM) for minimizing...
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02/22/2022
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations
Data augmentation is commonly applied to improve performance of deep lea...
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11/16/2021
HiRID-ICU-Benchmark – A Comprehensive Machine Learning Benchmark on High-resolution ICU Data
The recent success of machine learning methods applied to time series co...
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06/09/2021
Neighborhood Contrastive Learning Applied to Online Patient Monitoring
Intensive care units (ICU) are increasingly looking towards machine lear...
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05/19/2021
Boosting Variational Inference With Locally Adaptive Step-Sizes
Variational Inference makes a trade-off between the capacity of the vari...
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05/12/2021
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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04/11/2021
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning
Marginal-likelihood based model-selection, even though promising, is rar...
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02/12/2021
Bayesian Neural Network Priors Revisited
Isotropic Gaussian priors are the de facto standard for modern Bayesian ...
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02/10/2021
On Disentanglement in Gaussian Process Variational Autoencoders
Complex multivariate time series arise in many fields, ranging from comp...
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11/02/2020
WRSE – a non-parametric weighted-resolution 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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10/27/2020
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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10/26/2020
Scalable Gaussian Process Variational Autoencoders
Conventional variational autoencoders fail in modeling correlations betw...
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07/28/2020
A Commentary on the Unsupervised Learning of Disentangled Representations
The goal of the unsupervised learning of disentangled representations is...
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02/07/2020
Weakly-Supervised Disentanglement Without Compromises
Intelligent agents should be able to learn useful representations by obs...
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11/11/2019
Communication-Efficient Jaccard Similarity for High-Performance Distributed Genome Comparisons
Jaccard Similarity index is an important measure of the overlap of two s...
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10/03/2019
Variational PSOM: Deep Probabilistic Clustering with Self-Organizing Maps
Generating visualizations and interpretations from high-dimensional data...
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09/28/2019
Deep Multiple Instance Learning for Taxonomic Classification of Metagenomic read sets
Metagenomic studies have increasingly utilized sequencing technologies i...
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07/09/2019
Multivariate Time Series Imputation with Variational Autoencoders
Multivariate time series with missing values are common in many areas, f...
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05/03/2019
Disentangling Factors of Variation Using Few Labels
Learning disentangled representations is considered a cornerstone proble...
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04/29/2019
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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04/16/2019
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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01/23/2019
Deep Mean Functions for Meta-Learning in Gaussian Processes
Fitting machine learning models in the low-data limit is challenging. Th...
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10/24/2018
Scalable Gaussian Processes on Discrete Domains
Kernel methods on discrete domains have shown great promise for many cha...
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06/06/2018
Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series
Human professionals are often required to make decisions based on comple...
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06/06/2018
Boosting Black Box Variational Inference
Approximating a probability density in a tractable manner is a central t...
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04/30/2018
Clustering Meets Implicit Generative Models
Clustering is a cornerstone of unsupervised learning which can be though...
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03/26/2018
Revisiting First-Order Convex Optimization Over Linear Spaces
Two popular examples of first-order optimization methods over linear spa...
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08/05/2017
Boosting Variational Inference: an Optimization Perspective
Variational Inference is a popular technique to approximate a possibly i...
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06/08/2017
Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs
Generative Adversarial Networks (GANs) have shown remarkable success as ...
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05/31/2017
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees
Greedy optimization methods such as Matching Pursuit (MP) and Frank-Wolf...
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07/17/2016
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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02/10/2016
Knowledge Transfer with Medical Language Embeddings
Identifying relationships between concepts is a key aspect of scientific...
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10/01/2015
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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06/30/2015
Framework for Multi-task Multiple Kernel Learning and Applications in Genome Analysis
We present a general regularization-based framework for Multi-task learn...
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06/16/2015
Bayesian representation learning with oracle constraints
Representation learning systems typically rely on massive amounts of lab...
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05/28/2015
Automatic Relevance Determination For Deep Generative Models
A recurring problem when building probabilistic latent variable models i...
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04/14/2015
Probabilistic Clustering of Time-Evolving Distance Data
We present a novel probabilistic clustering model for objects that are r...
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09/17/2013