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20172022
most citedHow deep is deep enough? -- Quantifying class separability in the hidden layers of deep neural networks

61 citations · 266 across the 10 of their papers we have counts for

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2 papers · 1 filter

cs.NE2020★ 21 cited

Integration of Leaky-Integrate-and-Fire-Neurons in Deep Learning Architectures

Richard C. Gerum, Achim Schilling

Up to now, modern Machine Learning is mainly based on fitting high dimensional functions to enormous data sets, taking advantage of huge hardware resources. We show that biological…

cs.NE2019★ 59 cited

Sparsity through evolutionary pruning prevents neuronal networks from overfitting

Richard C. Gerum, André Erpenbeck, Patrick Krauss +1

Modern Machine learning techniques take advantage of the exponentially rising calculation power in new generation processor units. Thus, the number of parameters which are trained…