Use generative models to do some art from existing images or videos.

AutoEncoder+VAE

Handwritten Digit Generation with VAE and GAN are applied.

Implementation of generative models for the design of small molecules

single cell Hi-C, machine learning

PyTorch re-implementation of "Generating Sentences from a Continuous Space" by Bowman et al. (2015), using BART encoder and GRU decoder with beam search.

variational_autoencoder

Keras implementation of autoencoders

Some coding stuff from various machine learning books

The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for the purpose of dimensionality reduction.

Reimplementation of CoMA to be used for dimensionality reduction of cardiac meshes.

Deep Generative Models that I have implemented, trained and experimented with.

It's a repo for figuring out ways how to get automatic text summaries to Auto-ML models that perform regression (get compressed and factorized latent representation). Ideally it should be even able to answer questions on properties of that model.

Clock using MNIST Variational Auto-Encoder (VAE)

An implementation of the TME from the Reinforcement Learning course given at Sorbonne University.

Files related to CAP-6610: Machine Learning course at UFL

Repository meant to reproduce the research results of the paper "Towards Multi-User Activity Recognition through Facilitated Training Data and Deep Learning for Human-Robot Collaboration Applications". Please find the AAM at the following link:

VAE implementation for text generation with PyTorch

Aplicação da rede VAE + PCA para geração de faces de gatos

In this project, we have explored the research paper titled Variational Autoencoders for Collaborative Filtering and implemented its findings. We aim to further improve upon the proposed methodology to contribute to the advancement of personalized recommendations and push the boundaries of existing techniques.