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rnn-theanoRNN(LSTM, GRU) in Theano with mini-batch training; character-level language models in Theano
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TelemanomA framework for using LSTMs to detect anomalies in multivariate time series data. Includes spacecraft anomaly data and experiments from the Mars Science Laboratory and SMAP missions.
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dtsA Keras library for multi-step time-series forecasting.
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Eeg DlA Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.
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TsaiTime series Timeseries Deep Learning Pytorch fastai - State-of-the-art Deep Learning with Time Series and Sequences in Pytorch / fastai
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Wavetorch 🌊 Numerically solving and backpropagating through the wave equation
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Ad examplesA collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.
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Gru Svm[ICMLC 2018] A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection
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STAR Network[PAMI 2021] Gating Revisited: Deep Multi-layer RNNs That Can Be Trained
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Skip Thoughts.torchPorting of Skip-Thoughts pretrained models from Theano to PyTorch & Torch7
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HasteHaste: a fast, simple, and open RNN library
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myDLDeep Learning
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tf-ran-cellRecurrent Additive Networks for Tensorflow
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theano-recurrenceRecurrent Neural Networks (RNN, GRU, LSTM) and their Bidirectional versions (BiRNN, BiGRU, BiLSTM) for word & character level language modelling in Theano
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Theano Kaldi RnnTHEANO-KALDI-RNNs is a project implementing various Recurrent Neural Networks (RNNs) for RNN-HMM speech recognition. The Theano Code is coupled with the Kaldi decoder.
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RganRecurrent (conditional) generative adversarial networks for generating real-valued time series data.
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modulesThe official repository for our paper "Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks". We develop a method for analyzing emerging functional modularity in neural networks based on differentiable weight masks and use it to point out important issues in current-day neural networks.
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sequence-rnn-pySequence analyzing using Recurrent Neural Networks (RNN) based on Keras
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Deep-Learning-CourseraProjects from the Deep Learning Specialization from deeplearning.ai provided by Coursera
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talariaTalariaDB is a distributed, highly available, and low latency time-series database for Presto
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