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20122022
most citedA Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges

2.7k citations · 7k across the 44 of their papers we have counts for

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Showing 2016Show all

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math.ST2016★ 1 cited

Quantifying Heteroskedasticity via Bhattacharyya Distance

Marwa Hassan, Mo Hossny, Douglas Creighton +1

Heteroskedasticity is a statistical anomaly that describes differing variances of error terms in a time series dataset. The presence of heteroskedasticity in data imposes serious c…

cs.CV2016★ 38 cited

Multi-Residual Networks: Improving the Speed and Accuracy of Residual Networks

Masoud Abdi, Saeid Nahavandi

In this article, we take one step toward understanding the learning behavior of deep residual networks, and supporting the observation that deep residual networks behave like ensem…