28 citations · 60 across the 8 of their papers we have counts for
8 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…
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…
Sparse Representation Classification via Screening for Graphs
Cencheng Shen, Li Chen, Yuexiao Dong +1
The sparse representation classifier (SRC) is shown to work well for image recognition problems that satisfy a subspace assumption. In this paper we propose a new implementation of…
A Joint Graph Inference Case Study: the C.elegans Chemical and Electrical Connectomes
Li Chen, Joshua T. Vogelstein, Vince Lyzinski +1
We investigate joint graph inference for the chemical and electrical connectomes of the \textit{Caenorhabditis elegans} roundworm. The \textit{C.elegans} connectomes consist of $25…
Sparse Representation Classification Beyond L1 Minimization and the Subspace Assumption
Cencheng Shen, Li Chen, Yuexiao Dong +1
The sparse representation classifier (SRC) has been utilized in various classification problems, which makes use of L1 minimization and works well for image recognition satisfying…
Vertex nomination schemes for membership prediction
D. E. Fishkind, V. Lyzinski, H. Pao +2
Suppose that a graph is realized from a stochastic block model where one of the blocks is of interest, but many or all of the vertices' block labels are unobserved. The task is to…