KurochkinAlexey/SOM-VAE

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Updated over 5 years ago

KurochkinAlexey/SOM-VAE

KurochkinAlexey

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Pytorch implementation of SOM-VAE: INTERPRETABLE DISCRETE REPRESENTATION LEARNING ON TIME SERIES https://arxiv.org/pdf/1806.02199v7.pdf

IMV_LSTM

Pytorch implementation of "Exploring Interpretable LSTM Neural Networks over Multi-Variable Data" https://arxiv.org/pdf/1905.12034.pdf

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Hierarchical-Attention-Based-Recurrent-Highway-Networks-for-Time-Series-Prediction

Pytorch implementation of Hierarchical Attention-Based Recurrent Highway Networks for Time Series Prediction https://arxiv.org/abs/1806.00685

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ConvRNN

Pytorch implementation of Autoregressive Convolutional Recurrent Neural Network for Univariate and Multivariate Time Series Prediction https://arxiv.org/pdf/1907.04155.pdf

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Self-boosted-Time-series-Forecasting

Pytorch implementation of "Self-boosted Time-series Forecasting with Multi-task and Multi-view Learning" https://arxiv.org/pdf/1909.08181.pdf

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DA-RNN

Pytorch implementation of Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction https://arxiv.org/pdf/1704.02971.pdf

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Recurrent-neural-processes

Pytorch implementation of Recurrent Neural Processes https://arxiv.org/pdf/1906.05915.pdf

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AntisymmetricRNN

Python implementation of paper "AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks"

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Time-series-precision-recall

Python implementation of paper "Precision and Recall for Time Series" https://arxiv.org/abs/1803.03639

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Attentive-neural-processes

Pytorch implementation of Attentive Neural Processes https://arxiv.org/pdf/1901.05761.pdf

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discovering-long-term-dependencies-in-noisy-time-series-data

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