MOVE (Multi-Omics Variational autoEncoder) for integrating multi-omics data and identifying cross modal associations
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Updated
Oct 1, 2024 - Jupyter Notebook
MOVE (Multi-Omics Variational autoEncoder) for integrating multi-omics data and identifying cross modal associations
Joint variational Autoencoders for Multimodal Imputation and Embedding (JAMIE)
[Pytorch] Minimal implementation of a Variational Autoencoder (VAE) with Categorical Latent variables inspired from "Categorical Reparameterization with Gumbel-Softmax".
A collections of basic autoencoders and Generative models for chemistry
Approximate inference of latent non-Gaussian models
Discrete Variational Autoencoder in PyTorch
automatic/analytical differentiation benchmark
Anomaly detection in time series
Disentangling the latent space of a VAE.
Efficient C implementation of Quantum Analytic Descent
probabilistic graphical model collections
Notebooks exploring various features of the Rigetti Forest & Grove using pyQuil
Third year mathematics dissertation on variational, laplace and mcmc approximations of bayesian logistic regression
Disentangled Variational Auto-Encoder in TensorFlow / Keras (Beta-VAE)
Experiments on Disentangled Representation Learning using Variational autoencoding algorithms
Some basic implementations of Variational Autoencoders in pytorch
Code for Adversarial Approximate Inference for Speech to Laryngograph Conversion
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