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Noise Sensitivity and Stability of Deep Neural Networks for Binary Classification
A first step is taken towards understanding often observed non-robustness phenomena of deep neural net (DNN) classifiers.
Robust Neural Network Classification via Double Regularization
Here we propose a novel double regularization of the neural network training loss that combines a penalty on the complexity of the classification model and an optimal reweighting of training observations.
Bayesian leave-one-out cross-validation for large data
Model inference, such as model comparison, model checking, and model selection, is an important part of model development.
Fast mixing for Latent Dirichlet allocation
Then one samples from the given distribution by running the Markov chain for a "long time" until it appears to be stationary and then collects the sample.
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