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Found 4 papers, 1 papers with code
Deep Learning is Provably Robust to Symmetric Label Noise
We conjecture that for general label noise, mitigation strategies that make use of the noisy data will outperform those that ignore the noisy data.
Dynamic Network Sampling for Community Detection
We propose a dynamic network sampling scheme to optimize block recovery for stochastic blockmodel (SBM) in the case where it is prohibitively expensive to observe the entire graph.
Deep Learning with Label Noise: A Hierarchical Approach
Our hierarchical approach improves upon regular deep neural networks in learning with label noise.
On spectral algorithms for community detection in stochastic blockmodel graphs with vertex covariates
In network inference applications, it is often desirable to detect community structure, namely to cluster vertices into groups, or blocks, according to some measure of similarity.
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