811 citations · 4.2k across the 47 of their papers we have counts for
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Evaluating State-of-the-Art Classification Models Against Bayes Optimality
Ryan Theisen, Huan Wang, Lav R. Varshney +2
Evaluating the inherent difficulty of a given data-driven classification problem is important for establishing absolute benchmarks and evaluating progress in the field. To this end…
Neural Bayes: A Generic Parameterization Method for Unsupervised Representation Learning
Devansh Arpit, Huan Wang, Caiming Xiong +2
We introduce a parameterization method called Neural Bayes which allows computing statistical quantities that are in general difficult to compute and opens avenues for formulating…
Predicting with High Correlation Features
Devansh Arpit, Caiming Xiong, Richard Socher
It has been shown that instead of learning actual object features, deep networks tend to exploit non-robust (spurious) discriminative features that are shared between training and…
Global Capacity Measures for Deep ReLU Networks via Path Sampling
Ryan Theisen, Jason M. Klusowski, Huan Wang +3
Classical results on the statistical complexity of linear models have commonly identified the norm of the weights as a fundamental capacity measure. Generalizations of this…