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06/11/2021
Precise characterization of the prior predictive distribution of deep ReLU networks
Recent works on Bayesian neural networks (BNNs) have highlighted the nee...
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06/11/2021
Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect
The "cold posterior effect" (CPE) in Bayesian deep learning describes th...
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06/06/2021
A Primer on Multi-Neuron Relaxation-based Adversarial Robustness Certification
The existence of adversarial examples poses a real danger when deep neur...
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02/07/2020
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Variational Bayesian Inference is a popular methodology for approximatin...
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02/06/2020
How Good is the Bayes Posterior in Deep Neural Networks Really?
During the past five years the Bayesian deep learning community has deve...
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01/14/2020
Hydra: Preserving Ensemble Diversity for Model Distillation
Ensembles of models have been empirically shown to improve predictive pe...
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06/04/2019
Adversarial Training Generalizes Data-dependent Spectral Norm Regularization
We establish a theoretical link between adversarial training and operato...
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02/13/2019
The Odds are Odd: A Statistical Test for Detecting Adversarial Examples
We investigate conditions under which test statistics exist that can rel...
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05/22/2018
Adversarially Robust Training through Structured Gradient Regularization
We propose a novel data-dependent structured gradient regularizer to inc...
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05/25/2017
