EdoardoBotta/Gaussian-Mixture-VAE
[Pytorch] Minimal implementation of a Variational Autoencoder (VAE) with Categorical Latent variables inspired from "Categorical Reparameterization with Gumbel-Softmax".
This project helps machine learning engineers and researchers analyze complex time-series data. It takes in sequential data, such as sensor readings or financial prices, and identifies underlying, distinct patterns or 'modes' that govern how the data evolves over time. The output is a model that can explain these hidden patterns and potentially generate new data following the same rules.
No commits in the last 6 months.
Use this if you need to identify and model discrete, switching behaviors within continuous time-series data, where the exact 'state' influencing the data's evolution is not directly observed.
Not ideal if your data does not have a sequential, time-dependent nature or if you need a model that explicitly handles continuous latent variables.
Stars
7
Forks
3
Language
Python
License
—
Category
Last pushed
Jun 16, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/generative-ai/EdoardoBotta/Gaussian-Mixture-VAE"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
EdoardoBotta/RQ-VAE-Recommender
[Pytorch] Generative retrieval model using semantic IDs from "Recommender Systems with...
is0383kk/SymbolEmergence-VAE-GMM
Symbol emergence using Variational Auto-Encoder and Gaussian Mixture Model...
is0383kk/Dirichlet-VAE
Dirichlet-Variational Auto-Encoder by PyTorch
Bhavik-Ardeshna/pytorch-VAE
Variational Autoencoder and a Disentangled version (beta-VAE) implementation in PyTorch-Lightning
farhad-dalirani/Controllable-Face-Generation-VAE
A controllable generative model using a Convolutional Variational Autoencoder (VAE) with GUI to...