Directories
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Path | Synopsis |
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Higher Order Recurrent Neural Networks (HORN)
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Higher Order Recurrent Neural Networks (HORN) |
LSTM enriched with a PolicyGradient to enable Dynamic Skip Connections.
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LSTM enriched with a PolicyGradient to enable Dynamic Skip Connections. |
Implementation of the MIST (MIxed hiSTory) recurrent network as described in "Analyzing and Exploiting NARX Recurrent Neural Networks for Long-Term Dependencies" by Di Pietro et al., 2018 (https://arxiv.org/pdf/1702.07805.pdf).
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Implementation of the MIST (MIxed hiSTory) recurrent network as described in "Analyzing and Exploiting NARX Recurrent Neural Networks for Long-Term Dependencies" by Di Pietro et al., 2018 (https://arxiv.org/pdf/1702.07805.pdf). |
Implementation of the NRU (Non-Saturating Recurrent Units) recurrent network as described in "Towards Non-Saturating Recurrent Units for Modelling Long-Term Dependencies" by Chandar et al., 2019.
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Implementation of the NRU (Non-Saturating Recurrent Units) recurrent network as described in "Towards Non-Saturating Recurrent Units for Modelling Long-Term Dependencies" by Chandar et al., 2019. |
RLA (Recurrent Linear Attention) "Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention" by Katharopoulos et al., 2020.
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RLA (Recurrent Linear Attention) "Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention" by Katharopoulos et al., 2020. |
srnn implements the SRNN (Shuffling Recurrent Neural Networks) by Rotman and Wolf, 2020.
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srnn implements the SRNN (Shuffling Recurrent Neural Networks) by Rotman and Wolf, 2020. |
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