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![bit.ly/InsightDL2018
#InsightDL2018
Outline
Recurrent Neural
Networks (RNNs)
SkipRNN
[ICLR 2018]
RepeatRNN
[ICLRW 2018]](https://image.slidesharecdn.com/dlmmdcu2018skippingandrepeatingsamplesinrnns-180530113149/75/Skipping-and-Repeating-Samples-in-Recurrent-Neural-Networks-8-2048.jpg?cb=1666807760)
![bit.ly/InsightDL2018
#InsightDL2018
Outline
Recurrent Neural
Networks (RNNs)
SkipRNN
[ICLR 2018]
RepeatRNN
[ICLRW 2018]](https://image.slidesharecdn.com/dlmmdcu2018skippingandrepeatingsamplesinrnns-180530113149/75/Skipping-and-Repeating-Samples-in-Recurrent-Neural-Networks-9-2048.jpg?cb=1666807760)
![bit.ly/InsightDL2018
#InsightDL2018
Outline
Recurrent Neural
Networks (RNNs)
SkipRNN
[ICLR 2018]
RepeatRNN
[ICLRW 2018]](https://image.slidesharecdn.com/dlmmdcu2018skippingandrepeatingsamplesinrnns-180530113149/75/Skipping-and-Repeating-Samples-in-Recurrent-Neural-Networks-19-2048.jpg?cb=1666807760)
![bit.ly/InsightDL2018
#InsightDL2018
Outline
Recurrent Neural
Networks (RNNs)
SkipRNN
[ICLR 2018]
RepeatRNN
[ICLRW 2018]](https://image.slidesharecdn.com/dlmmdcu2018skippingandrepeatingsamplesinrnns-180530113149/75/Skipping-and-Repeating-Samples-in-Recurrent-Neural-Networks-37-2048.jpg?cb=1666807760)
![bit.ly/InsightDL2018
#InsightDL2018
Outline
Recurrent Neural
Networks (RNNs)
SkipRNN
[ICLR 2018]
RepeatRNN
[ICLRW 2018]](https://image.slidesharecdn.com/dlmmdcu2018skippingandrepeatingsamplesinrnns-180530113149/75/Skipping-and-Repeating-Samples-in-Recurrent-Neural-Networks-48-2048.jpg?cb=1666807760)
![bit.ly/InsightDL2018
#InsightDL2018
SkipRNN
Update state gate ∈ {0, 1}
Update state probability ∈ [0, 1]](https://image.slidesharecdn.com/dlmmdcu2018skippingandrepeatingsamplesinrnns-180530113149/75/Skipping-and-Repeating-Samples-in-Recurrent-Neural-Networks-55-2048.jpg?cb=1666807760)
![bit.ly/InsightDL2018
#InsightDL2018
SkipRNN
Update state gate ∈ {0, 1}
Update state probability ∈ [0, 1]
Increment for the...](https://image.slidesharecdn.com/dlmmdcu2018skippingandrepeatingsamplesinrnns-180530113149/75/Skipping-and-Repeating-Samples-in-Recurrent-Neural-Networks-56-2048.jpg?cb=1666807760)
![bit.ly/InsightDL2018
#InsightDL2018
SkipRNN
Update state gate ∈ {0, 1}
Update state probability ∈ [0, 1]
Increment for the...](https://image.slidesharecdn.com/dlmmdcu2018skippingandrepeatingsamplesinrnns-180530113149/75/Skipping-and-Repeating-Samples-in-Recurrent-Neural-Networks-57-2048.jpg?cb=1666807760)
