Lstm variational auto-encoder for time series anomaly detection and features extraction

Keras implementation of LSTM Variational Autoencoder

A Simple Pytorch Implementation of LSTM-based Variational Autoencoder(VAE)

A PyTorch Implementation of Generating Sentences from a Continuous Space by Bowman et al. 2015.

music generation with a classifying variational autoencoder (VAE) and LSTM

Matlab Variational LSTM Autoencoder and Time Series Prediction for anomaly detection. Some code of my masters thesis. Download Link: https://pure.unileoben.ac.at/portal/files/6093740/AC16131071.pdf

LSTM Variational AutoEncoder

LSTM-based Variational Autoencoder for Text Encoding

Simultaneous Classification and Regression with Variational Autoencoder and LSTM

An implementation of a Standard and a Variational LSTM Autoencoder for generating passwords.

Variational autoencoder LSTMs for time series data.

Long short term memory (LSTM) and Variational-autoencoder (VAR) combined for Multivariate Time Series Forecasting

Experiment with some approaches of music generation based on deep learning techniques: a LSTM, a Variational Autoencoder, a GAN and a novel architecture called MidiNet. 🎵

This repository consists of application of Deep Learning Models like DNN, CNN (1D and 2D), RNN (LSTM and GRU) and Variational Autoencoders written from scratch in tensorflow.

variational Auto Encoder for textual pattern using lstm embedding

Official implementation of our 2022 IEEE LA-CCI paper Student Dropout Prediction using 1D CNN-LSTM with Variational Autoencoder Oversampling.

A Sequence to Sequence Variational Autoencoder that encodes strokes (of a sketch) into a latent vector space using a bidirectional LSTM to produce sketches of common objects.

Variational Autoencoder (VAE) with LSTM layers to generate short dance phrases. Compare input sequences from a test set with their decoded counterparts using the VAE model. Generate new dance sequences using the trained model and visualize them alongside real sequences for evaluation.

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Variational Autoencoder (VAE) for MNIST using LSTM encoder and CNN decoder

TensorFlow v2 implementation of a hybrid Variational Autoencoder with Long Short-Term Memory (VAE-LSTM) for sequential data. Includes training scripts and dataset loader.

MGU-V is a deep learning-based framework for generating Lo-Fi music using a hybrid approach combining Long Short-Term Memory (LSTM) networks and Variational Autoencoders (VAEs). This model is capable of producing high-quality music by learning from a curated MIDI dataset

Variational Seq2Seq model

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variational autoencoder with lstm cells

SMILES sequence reconstruction using LSTM Autoencoder & Variational Autoencoder

Generation of phrase with a Variational AutoEncoder based on LSTM

Bayesian deep learning classifies extreme wind events in coastal cities, combining statistical rigor with uncertainty-aware modeling for better disaster preparedness and climate adaptation.

Image Generation From Text Using Variational Autoencoder and LSTM (Long Short-Term Memory) models

Bidirectional LSTM and Variational Autoencoder-Based Spatiotemporal Anomaly Detection System for Mine Borehole Water Level Monitoring