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Found 8 papers, 1 papers with code
On the Challenges and Opportunities in Generative AI
The field of deep generative modeling has grown rapidly and consistently over the years.
On the Limitations of Model Stealing with Uncertainty Quantification Models
We realize that during training, the models tend to have similar predictions, indicating that the network diversity we wanted to leverage using uncertainty quantification models is not (high) enough for improvements on the model stealing task.
How Sampling Impacts the Robustness of Stochastic Neural Networks
Stochastic neural networks (SNNs) are random functions whose predictions are gained by averaging over multiple realizations.
SmoothLRP: Smoothing Explanations of Neural Network Decisions by Averaging over Stochastic Input Variations
With the excessive use of neural networks in safety critical domains the need for understandable explanations of their predictions is rising.
Efficient Calculation of Adversarial Examples for Bayesian Neural Networks
In this paper we propose to efficiently attack Bayesian neural networks with adversarial examples calculated for a deterministic network with parameters given by the mean of the posterior distribution.
Investigating maximum likelihood based training of infinite mixtures for uncertainty quantification
Uncertainty quantification in neural networks gained a lot of attention in the past years.
Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification
The neural networks for uncertainty quantification simultaneously diminish the vulnerability to the attack, which is reflected in a lower recognition accuracy of the malicious target text in comparison to a standard hybrid ASR system.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Predictive Uncertainty Quantification with Compound Density Networks
Despite the huge success of deep neural networks (NNs), finding good mechanisms for quantifying their prediction uncertainty is still an open problem.
