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![Model description
11
Update state gate ∈ {0, 1}
Update state probability ∈ [0, 1]](https://image.slidesharecdn.com/cvcskiprnn-slides-180608085312/75/Skip-RNN-Learning-to-Skip-State-Updates-in-RNNs-ICLR-2018-11-2048.jpg?cb=1666804902)
![Model description
12
Update state gate ∈ {0, 1}
Update state probability ∈ [0, 1]](https://image.slidesharecdn.com/cvcskiprnn-slides-180608085312/75/Skip-RNN-Learning-to-Skip-State-Updates-in-RNNs-ICLR-2018-12-2048.jpg?cb=1666804902)
![Model description
13
Update state gate ∈ {0, 1}
Update state probability ∈ [0, 1]
Increment for the update state probabili...](https://image.slidesharecdn.com/cvcskiprnn-slides-180608085312/75/Skip-RNN-Learning-to-Skip-State-Updates-in-RNNs-ICLR-2018-13-2048.jpg?cb=1666804902)
![Model description
14
Update state gate ∈ {0, 1}
Update state probability ∈ [0, 1]
Increment for the update state probabili...](https://image.slidesharecdn.com/cvcskiprnn-slides-180608085312/75/Skip-RNN-Learning-to-Skip-State-Updates-in-RNNs-ICLR-2018-14-2048.jpg?cb=1666804902)
![Sequential MNIST: overview
▷ Digit classification task with 10 classes, i.e.
[0, 9]
▷ Traditionally addressed with CNNs, b...](https://image.slidesharecdn.com/cvcskiprnn-slides-180608085312/75/Skip-RNN-Learning-to-Skip-State-Updates-in-RNNs-ICLR-2018-22-2048.jpg?cb=1666804902)
