A Tensorflow implementation of a Variational Autoencoder
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Updated
Sep 28, 2017 - Jupyter Notebook
A Tensorflow implementation of a Variational Autoencoder
Implementation of a Basic Variational Auto-Encoder
This repository aims to make analysis on human brain tissue (EM data). This work is done within the chair of Lichtman Lab. at Harvard University.
Final project for Bayesian Theory and Computation (2021 spring) @ PKU.
Deep generative models for controlled text generation.
unsupervised semantic segmentation for self driving cars with variational autoencoders, genetic algorithms and bayesian methods
Implementing Variational Autoencoder and explored the importance of each part of its loss function.
Handwritten Digit Generation with VAE and GAN are applied.
Demo Page for "Generative Models for Improved Naturalness, Intelligibility, and Voicing of Whispered Speech" (SLT22)
Implementation of generative models for the design of small molecules
Pytorch Implementation of the World Models paper from 2018.
Implementation of a Denoising Diffusion Probabilistic Model with some mathematical background.
Variational Autoencoder that is trained to generate or reconstruct audio of spoken digits from 0 to 9
Repo for all the SRIP 2024 work at CVIG Lab IITGN under Prof. Shanmuganathan Raman
A PyTorch implementation of neural dialogue system using conditional variational autoencoder (CVAE)
My personal experiments with variational autoencoders.
Implementation of deep learning models using Pytorch
Tensorflow 2.x implementation of DFCVAE
Model Pipelines for GNNs, VAEs, Neural Style Transfer, and other kinds of models!
Implementation of a simple Variational Auto Encoder using ELBO
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