24 citations · 55 across the 21 of their papers we have counts for
Showing stat.MLShow all
2 papers · 1 filter
stat.ML2020
Ramifications of Approximate Posterior Inference for Bayesian Deep Learning in Adversarial and Out-of-Distribution Settings
John Mitros, Arjun Pakrashi, Brian Mac Namee
Deep neural networks have been successful in diverse discriminative classification tasks, although, they are poorly calibrated often assigning high probability to misclassified pre…
stat.ML2019★ 24 cited
On the Validity of Bayesian Neural Networks for Uncertainty Estimation
John Mitros, Brian Mac Namee
Deep neural networks (DNN) are versatile parametric models utilised successfully in a diverse number of tasks and domains. However, they have limitations---particularly from their…