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Found 5 papers, 1 papers with code
Tree Bandits for Generative Bayes
Finally, we successfully apply our approach to the problem of masked image classification using deep generative models.
Deep Bayes Factors
After training, our deep learning approach enables rapid evaluations of the Bayes factor estimator at any fictional data arriving from either hypothesized model, not just the observed data $Y_0$.
On Mixing Rates for Bayesian CART
We show polynomial mixing of Twiggy Bayesian CART without assuming that the signal is connected on a tree.
Robust Sensible Adversarial Learning of Deep Neural Networks for Image Classification
Specifically, we define a sensible adversary which is useful for learning a robust model while keeping high natural accuracy.
Sensible adversarial learning
The trade-off between robustness and standard accuracy has been consistently reported in the machine learning literature.
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