MOVE (Multi-Omics Variational autoEncoder) for integrating multi-omics data and identifying cross modal associations
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Updated
Aug 14, 2024 - Jupyter Notebook
MOVE (Multi-Omics Variational autoEncoder) for integrating multi-omics data and identifying cross modal associations
Disentangled Variational Auto-Encoder in TensorFlow / Keras (Beta-VAE)
Implementations of autoencoder, generative adversarial networks, variational autoencoder and adversarial variational autoencoder
TensorFlow implementation of the method from Variational Dropout Sparsifies Deep Neural Networks, Molchanov et al. (2017)
Deep Probabilistic Programming Examples in Pytorch using pyro
Joint variational Autoencoders for Multimodal Imputation and Embedding (JAMIE)
probabilistic graphical model collections
Disentangling the latent space of a VAE.
Some basic implementations of Variational Autoencoders in pytorch
Code for Adversarial Approximate Inference for Speech to Laryngograph Conversion
Efficient C implementation of Quantum Analytic Descent
automatic/analytical differentiation benchmark
Anomaly detection in time series
A simple variational autoencoder to generate images from MNIST. Implemented in TensorFlow.
Discrete Variational Autoencoder in PyTorch
Python toolbox for solving imaging continuous optimization problems.
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