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Found 8 papers, 3 papers with code
Hospitalization Length of Stay Prediction using Patient Event Sequences
Predicting patients hospital length of stay (LOS) is essential for improving resource allocation and supporting decision-making in healthcare organizations.
Graph Neural Networks for Microbial Genome Recovery
In this paper, we propose to use Graph Neural Networks (GNNs) to leverage the assembly graph when learning contig representations for metagenomic binning.
Inducing Gaussian Process Networks
Gaussian processes (GPs) are powerful but computationally expensive machine learning models, requiring an estimate of the kernel covariance matrix for every prediction.
UniTE -- The Best of Both Worlds: Unifying Function-Fitting and Aggregation-Based Approaches to Travel Time and Travel Speed Estimation
An empirical study finds that an instance of UniTE can improve the accuracies of travel speed distribution and travel time estimation by $40-64\%$ and $3-23\%$, respectively, compared to using function fitting or aggregation alone
Relational Fusion Networks: Graph Convolutional Networks for Road Networks
The application of machine learning techniques in the setting of road networks holds the potential to facilitate many important intelligent transportation applications.
On Network Embedding for Machine Learning on Road Networks: A Case Study on the Danish Road Network
This is problematic for analysis tasks that rely on such information for machine learning.
Graph Convolutional Networks for Road Networks
In addition, we provide experimental evidence of the short-comings of state-of-the-art GCNs in the context of road networks: unlike our method, they cannot effectively leverage the road network structure for road segment classification and fail to outperform a regular multi-layer perceptron.
Adaptive User-Oriented Direct Load-Control of Residential Flexible Devices
First, DR schemes are highly demanding for the users, as users need to provide direct information, e. g. via surveys, on their energy consumption preferences.
