1 citations · 1 across the 4 of their papers we have counts for
4 papers
Deep Learning is Provably Robust to Symmetric Label Noise
Carey E. Priebe, Ningyuan Huang, Soledad Villar +2
Deep neural networks (DNNs) are capable of perfectly fitting the training data, including memorizing noisy data. It is commonly believed that memorization hurts generalization. The…
Dynamic Network Sampling for Community Detection
Cong Mu, Youngser Park, Carey E. Priebe
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
Li Chen, Ningyuan Huang, Cong Mu +4
Deep neural networks are susceptible to label noise. Existing methods to improve robustness, such as meta-learning and regularization, usually require significant change to the net…
On spectral algorithms for community detection in stochastic blockmodel graphs with vertex covariates
Cong Mu, Angelo Mele, Lingxin Hao +3
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.…