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Found 10 papers, 4 papers with code
Explicit Gradient Learning for Black-Box Optimization
We prove the convergence of EGL to a stationary point and its robustness in the optimization of integrable functions.
Family based HLA imputation and optimization of haplo-identical transplants
We use GRAMM to detect recombination events and show that the rate of falsely detected recombination events (False Positive Rate) in simulations is very low.
Planted Dense Subgraphs in Dense Random Graphs Can Be Recovered using Graph-based Machine Learning
We show that PYGON can recover cliques of sizes $\Theta\left(\sqrt{n}\right)$, where $n$ is the size of the background graph, comparable with the state of the art.
High fraction of silent recombination in a finite population two-locus neutral birth-death-mutation model
Silent recombination considerably reduces the total number of haplotypes expected from the infinite site model for populations that are not much larger than one over the mutation rate.
Quadratic GCN for Graph Classification
We here propose a novel solution combining GCN, methods from knowledge graphs, and a new self-regularized activation function to significantly improve the accuracy of the GCN based GCT.
Evaluation of the number of undiagnosed infected in an outbreak using source of infection measurements
In times of outbreaks, an essential requirement for better monitoring is the evaluation of the number of undiagnosed infected individuals.
Explicit Gradient Learning
Instead of fitting the function, EGL trains a NN to estimate the objective gradient directly.
Topological based classification using graph convolutional networks
First, we show that even in the absence of any external information on nodes, a good accuracy can be obtained on the prediction of the node class using either topological features, or using the neighbors class as an input to a GCN.
Regional based query in graph active learning
As such, the uncertainty in the class of a node's neighbor may be a more appropriate selection criterion.
Topological based classification of paper domains using graph convolutional networks
The main approaches for node classification in graphs are information propagation and the association of the class of the node with external information.
