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)
Joint variational Autoencoders for Multimodal Imputation and Embedding (JAMIE)
Implementations of autoencoder, generative adversarial networks, variational autoencoder and adversarial variational autoencoder
Some basic implementations of Variational Autoencoders in pytorch
automatic/analytical differentiation benchmark
Deep Probabilistic Programming Examples in Pytorch using pyro
Third year mathematics dissertation on variational, laplace and mcmc approximations of bayesian logistic regression
TensorFlow implementation of the method from Variational Dropout Sparsifies Deep Neural Networks, Molchanov et al. (2017)
Python toolbox for solving imaging continuous optimization problems.
Variational AutoEncoder with Population Queue and AVS
Code for Adversarial Approximate Inference for Speech to Laryngograph Conversion
Mixture of Bayes PCA | Variational Inference
Disentangling the latent space of a VAE.
Mathematica code to generate the matrix elements required to build the kinetic energy operator (KEO) of linear molecule HCCH for use in numerical ro-vibrational calculations. The elements are Taylor-expanded in a set of 3N-6 internal coordinates (this can be amended to 3N-5) up to a user-defined expansion order
[Pytorch] Minimal implementation of a Variational Autoencoder (VAE) with Categorical Latent variables inspired from "Categorical Reparameterization with Gumbel-Softmax".
Discrete Variational Autoencoder in PyTorch
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