3 citations · 3 across the 4 of their papers we have counts for
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cs.LG2021
Gradient representations in ReLU networks as similarity functions
Dániel Rácz, Bálint Daróczy
Feed-forward networks can be interpreted as mappings with linear decision surfaces at the level of the last layer. We investigate how the tangent space of the network can be exploi…
cs.LG2020
Tangent Space Sensitivity and Distribution of Linear Regions in ReLU Networks
Bálint Daróczy
Recent articles indicate that deep neural networks are efficient models for various learning problems. However they are often highly sensitive to various changes that cannot be det…
cs.LG2019★ 3 cited
Tangent Space Separability in Feedforward Neural Networks
Bálint Daróczy, Rita Aleksziev, András Benczúr
Hierarchical neural networks are exponentially more efficient than their corresponding "shallow" counterpart with the same expressive power, but involve huge number of parameters a…