A simple variational autoencoder to generate images from MNIST. Implemented in TensorFlow.
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Updated
Mar 5, 2017 - Python
A simple variational autoencoder to generate images from MNIST. Implemented in TensorFlow.
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)
Python toolbox for solving imaging continuous optimization problems.
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
Mixture of Bayes PCA | Variational Inference
Deep Probabilistic Programming Examples in Pytorch using pyro
Variational AutoEncoder with Population Queue and AVS
Code for Adversarial Approximate Inference for Speech to Laryngograph Conversion
Some basic implementations of Variational Autoencoders in pytorch
Experiments on Disentangled Representation Learning using Variational autoencoding algorithms
Disentangled Variational Auto-Encoder in TensorFlow / Keras (Beta-VAE)
Third year mathematics dissertation on variational, laplace and mcmc approximations of bayesian logistic regression
Notebooks exploring various features of the Rigetti Forest & Grove using pyQuil
probabilistic graphical model collections
Efficient C implementation of Quantum Analytic Descent
Disentangling the latent space of a VAE.
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