Directories
¶
| Path | Synopsis |
|---|---|
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encoding
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fofe
Package fofe provides an implementation of the Fixed-size Ordinally-Forgetting Encoding (FOFE) method.
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Package fofe provides an implementation of the Fixed-size Ordinally-Forgetting Encoding (FOFE) method. |
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attention/lshattention
Package lshattention provides an implementation of the LSH-Attention model, as describe in `Reformer: The Efficient Transformer` by N. Kitaev, Ł. Kaiser, A. Levskaya (https://arxiv.org/pdf/2001.04451.pdf).
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Package lshattention provides an implementation of the LSH-Attention model, as describe in `Reformer: The Efficient Transformer` by N. Kitaev, Ł. Kaiser, A. Levskaya (https://arxiv.org/pdf/2001.04451.pdf). |
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attention/syntheticattention
Package syntheticattention provides an implementation of the Synthetic Attention described in: "SYNTHESIZER: Rethinking Self-Attention in Transformer Models" by Tay et al., 2020.
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Package syntheticattention provides an implementation of the Synthetic Attention described in: "SYNTHESIZER: Rethinking Self-Attention in Transformer Models" by Tay et al., 2020. |
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birnncrf
Package birnncrf provides an implementation of a Bidirectional Recurrent Neural Network (BiRNN) with a Conditional Random Fields (CRF) on tom.
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Package birnncrf provides an implementation of a Bidirectional Recurrent Neural Network (BiRNN) with a Conditional Random Fields (CRF) on tom. |
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bls
Package bls provides an implementation of the Broad Learning System (BLS) described in "Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture" by C. L. Philip Chen and Zhulin Liu, 2017.
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Package bls provides an implementation of the Broad Learning System (BLS) described in "Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture" by C. L. Philip Chen and Zhulin Liu, 2017. |
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gnn/slstm
Package slstm implements a Sentence-State LSTM graph neural network.
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Package slstm implements a Sentence-State LSTM graph neural network. |
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gnn/startransformer
Package startransformer provides a variant implementation of the Star-Transformer model introduced by Qipeng Guo, Xipeng Qiu et al.
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Package startransformer provides a variant implementation of the Star-Transformer model introduced by Qipeng Guo, Xipeng Qiu et al. |
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normalization/adanorm
Package adanorm implements the Adaptive Normalization (AdaNorm) method.
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Package adanorm implements the Adaptive Normalization (AdaNorm) method. |
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normalization/fixnorm
Package fixnorm implements the fixnorm normalization method.
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Package fixnorm implements the fixnorm normalization method. |
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normalization/layernorm
Package layernorm implements the Layer Normalization (LayerNorm) i method.
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Package layernorm implements the Layer Normalization (LayerNorm) i method. |
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normalization/layernormsimple
Package layernormsimple implements a simple version of LayerNorm (LayerNorm-simple).
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Package layernormsimple implements a simple version of LayerNorm (LayerNorm-simple). |
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normalization/rmsnorm
Package rmsnorm implements the Root Mean Square Layer Normalization method.
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Package rmsnorm implements the Root Mean Square Layer Normalization method. |
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rae
Package rae provides an implementation of the recursive auto-encoder strategy described in "Towards Lossless Encoding of Sentences" by Prato et al., 2019.
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Package rae provides an implementation of the recursive auto-encoder strategy described in "Towards Lossless Encoding of Sentences" by Prato et al., 2019. |
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rc
Package rc contains built-in Residual Connections (RC).
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Package rc contains built-in Residual Connections (RC). |
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recurrent/horn
Package horn provides an implementation of Higher Order Recurrent Neural Networks (HORN).
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Package horn provides an implementation of Higher Order Recurrent Neural Networks (HORN). |
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recurrent/lstmsc
Package lstmsc provides an implementation of LSTM enriched with a PolicyGradient to enable Dynamic Skip Connections.
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Package lstmsc provides an implementation of LSTM enriched with a PolicyGradient to enable Dynamic Skip Connections. |
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recurrent/mist
Package mist provides an 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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Package mist provides an 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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recurrent/nru
Package nru provides an 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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Package nru provides an 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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recurrent/rla
Package rla provides an implementation of RLA (Recurrent Linear Attention).
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Package rla provides an implementation of RLA (Recurrent Linear Attention). |
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recurrent/srnn
Package srnn implements the SRNN (Shuffling Recurrent Neural Networks) by Rotman and Wolf, 2020.
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Package srnn implements the SRNN (Shuffling Recurrent Neural Networks) by Rotman and Wolf, 2020. |
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