22 citations · 52 across the 12 of their papers we have counts for
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cs.LG2021★ 15 cited
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks
Pál András Papp, Karolis Martinkus, Lukas Faber +1
This paper studies Dropout Graph Neural Networks (DropGNNs), a new approach that aims to overcome the limitations of standard GNN frameworks. In DropGNNs, we execute multiple runs…
cs.CL2021
On Isotropy Calibration of Transformers
Yue Ding, Karolis Martinkus, Damian Pascual +2
Different studies of the embedding space of transformer models suggest that the distribution of contextual representations is highly anisotropic - the embeddings are distributed in…