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cs.LG2020
Matrix Shuffle-Exchange Networks for Hard 2D Tasks
Emīls Ozoliņš, Kārlis Freivalds, Agris Šostaks
Convolutional neural networks have become the main tools for processing two-dimensional data. They work well for images, yet convolutions have a limited receptive field that preven…
cs.LG2020
Residual Shuffle-Exchange Networks for Fast Processing of Long Sequences
Andis Draguns, Emīls Ozoliņš, Agris Šostaks +2
Attention is a commonly used mechanism in sequence processing, but it is of O(n^2) complexity which prevents its application to long sequences. The recently introduced neural Shuff…