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Found 6 papers, 0 papers with code
Deep Learning Framework with Uncertainty Quantification for Survey Data: Assessing and Predicting Diabetes Mellitus Risk in the American Population
The objectives of this paper are: (i) To propose a general predictive framework for regression and classification using neural network (NN) modeling, which incorporates survey weights into the estimation process; (ii) To introduce an uncertainty quantification algorithm for model prediction, tailored for data from complex survey designs; (iii) To apply this method in developing robust risk score models to assess the risk of Diabetes Mellitus in the US population, utilizing data from the NHANES 2011-2014 cohort.
kNN Algorithm for Conditional Mean and Variance Estimation with Automated Uncertainty Quantification and Variable Selection
In this paper, we introduce a kNN-based regression method that synergizes the scalability and adaptability of traditional non-parametric kNN models with a novel variable selection technique.
Prompting LLMs with content plans to enhance the summarization of scientific articles
This paper presents novel prompting techniques to improve the performance of automatic summarization systems for scientific articles.
Encoder-Decoder Model for Suffix Prediction in Predictive Monitoring
Predictive monitoring is a subfield of process mining that aims to predict how a running case will unfold in the future.
Gradual Drift Detection in Process Models Using Conformance Metrics
Changes, planned or unexpected, are common during the execution of real-life processes.
Embedding Graph Convolutional Networks in Recurrent Neural Networks for Predictive Monitoring
Predictive monitoring of business processes is a subfield of process mining that aims to predict, among other things, the characteristics of the next event or the sequence of next events.
