1 citations · 2 across the 6 of their papers we have counts for
7 papers
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…
Mental State Classification Using Multi-graph Features
Guodong Chen, Hayden S. Helm, Kate Lytvynets +2
We consider the problem of extracting features from passive, multi-channel electroencephalogram (EEG) devices for downstream inference tasks related to high-level mental states suc…
Leveraging semantically similar queries for ranking via combining representations
Hayden S. Helm, Marah Abdin, Benjamin D. Pedigo +8
In modern ranking problems, different and disparate representations of the items to be ranked are often available. It is sensible, then, to try to combine these representations to…
Inducing a hierarchy for multi-class classification problems
Hayden S. Helm, Weiwei Yang, Sujeeth Bharadwaj +5
In applications where categorical labels follow a natural hierarchy, classification methods that exploit the label structure often outperform those that do not. Un-fortunately, the…
A partition-based similarity for classification distributions
Hayden S. Helm, Ronak D. Mehta, Brandon Duderstadt +5
Herein we define a measure of similarity between classification distributions that is both principled from the perspective of statistical pattern recognition and useful from the pe…
Vertex Classification on Weighted Networks
Hayden Helm, Joshua Vogelstein, Carey Priebe
This paper proposes a discrimination technique for vertices in a weighted network. We assume that the edge weights and adjacencies in the network are conditionally independent and…