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Found 8 papers, 3 papers with code
A Masked language model for multi-source EHR trajectories contextual representation learning
Using electronic health records data and machine learning to guide future decisions needs to address challenges, including 1) long/short-term dependencies and 2) interactions between diseases and interventions.
Towards Explaining Satellite Based Poverty Predictions with Convolutional Neural Networks
Deep convolutional neural networks (CNNs) have been shown to predict poverty and development indicators from satellite images with surprising accuracy.
Satellite Image and Machine Learning based Knowledge Extraction in the Poverty and Welfare Domain
Our review of the field shows that the status of the three core elements of explainable machine learning (transparency, interpretability and domain knowledge) is varied and does not completely fulfill the requirements set up for scientific insights and discoveries.
The Concordance Index decomposition: A measure for a deeper understanding of survival prediction models
In contrast to such deep learning methods, classical machine learning models deteriorate when the censoring level decreases due to their inability to improve on ranking the events versus other events.
A New Bandit Setting Balancing Information from State Evolution and Corrupted Context
We present an algorithm that uses a referee to dynamically combine the policies of a contextual bandit and a multi-armed bandit.
Variational auto-encoders with Student's t-prior
We propose a new structure for the variational auto-encoders (VAEs) prior, with the weakly informative multivariate Student's t-distribution.
Establishing strong imputation performance of a denoising autoencoder in a wide range of missing data problems
Dealing with missing data in data analysis is inevitable.
The advantage of using Student's t-priors in variational autoencoders
Is it optimal to use the standard Gaussian prior in variational autoencoders?
