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Found 14 papers, 8 papers with code
Leveraging graph neural networks for supporting Automatic Triage of Patients
Triage methods are generally performed by human operator based on her own experience and information that are gathered from the patient management process.
Improving embedding of graphs with missing data by soft manifolds
The reliability of graph embeddings directly depends on how much the geometry of the continuous space matches the graph structure.
Interpretable Neural-Symbolic Concept Reasoning
Deep learning methods are highly accurate, yet their opaque decision process prevents them from earning full human trust.
Survey on Leveraging Uncertainty Estimation Towards Trustworthy Deep Neural Networks: The Case of Reject Option and Post-training Processing
In this paper, we present a systematic review of the prediction with the reject option in the context of various neural networks.
On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology
Our analysis provides a unified framework to study different recent methods introduced to cope with over-squashing and serves as a justification for a class of methods that fall under graph rewiring.
Graph Representation Learning on Tissue-Specific Multi-Omics
Overall, the combination of RNA-sequencing and gene methylation data leads to a link prediction accuracy of 71% on GGI networks.
MIASSR: An Approach for Medical Image Arbitrary Scale Super-Resolution
We also employ transfer learning to enable MIASSR to tackle SR tasks of new medical modalities, such as cardiac MR images (ACDC) and chest computed tomography images (COVID-CT).
Learning Mobility Flows from Urban Features with Spatial Interaction Models and Neural Networks
A fundamental problem of interest to policy makers, urban planners, and other stakeholders involved in urban development projects is assessing the impact of planning and construction activities on mobility flows.
Social and Information Networks Physics and Society
Arbitrary Scale Super-Resolution for Brain MRI Images
A recent paper has proposed a novel meta-learning technique that uses a Weight Prediction Network to enable Super-Resolution on arbitrary scale factors using only a single neural network.
A machine learning approach to investigate regulatory control circuits in bacterial metabolic pathways
In this work, a machine learning approach for identifying the multi-omics metabolic regulatory control circuits inside the pathways is described.
Towards Probabilistic Generative Models Harnessing Graph Neural Networks for Disease-Gene Prediction
Disease-gene prediction (DGP) refers to the computational challenge of predicting associations between genes and diseases.
Clique pooling for graph classification
We propose a novel graph pooling operation using cliques as the unit pool.
Prior and Likelihood Choices for Bayesian Matrix Factorisation on Small Datasets
These choices can greatly influence the predictive performance of the methods.
Fast Bayesian Non-Negative Matrix Factorisation and Tri-Factorisation
We present a fast variational Bayesian algorithm for performing non-negative matrix factorisation and tri-factorisation.
