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20152024
most citedA Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges

2.7k citations · 3.4k across the 32 of their papers we have counts for

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Showing 2019Show all

2 papers · 1 filter

cs.LG2019★ 16 cited

Potential adversarial samples for white-box attacks

Amir Nazemi, Paul Fieguth

Deep convolutional neural networks can be highly vulnerable to small perturbations of their inputs, potentially a major issue or limitation on system robustness when using deep net…

cs.CV2019

Assessing Architectural Similarity in Populations of Deep Neural Networks

Audrey Chung, Paul Fieguth, Alexander Wong

Evolutionary deep intelligence has recently shown great promise for producing small, powerful deep neural network models via the synthesis of increasingly efficient architectures o…