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3d-shapes

This repository contains the 3D shapes dataset, used in Kim, Hyunjik and Mnih, Andriy. "Disentangling by Factorising." In Proceedings of the 35th International Conference on Machine Learning (ICML). 2018. to assess the disentanglement properties of unsupervised learning methods.

benchmark_vae

Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

beta-vae

Pytorch implementation of β-VAE

coil_gecco22

code for paper: Peter J Bentley, Soo Ling Lim, Adam Gaier and Linh Tran. 2022. COIL: Constrained Optimization in Workshop on Learned Latent Space: Learning Representations for Valid Solutions. In Genetic and Evolutionary Computation Conference Companion (GECCO ’22 Companion). ACM, Boston, USA

constrained-graph-variational-autoencoder

Sample code for Constrained Graph Variational Autoencoders

cvae-for-molecular-design

Explore our CVAETF-based molecular design method, trained on 1.58M MOSES molecules. Tailored for property and structural constraints, it incorporates logP, tPSA, QED, and Murcko scaffold considerations using RDKit. Welcome to precise, data-driven molecular design.

cvae-opt

Learning a Latent Search Space for Routing Problems using Variational Autoencoders

dalle-pytorch

Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch

dsprites-dataset

Dataset to assess the disentanglement properties of unsupervised learning methods

g-means

gmm_dae

interviewassessement

This is an interview assessment.

machine-learning-interview

Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.

mae

PyTorch implementation of MAE https//arxiv.org/abs/2111.06377

mae_st

Official Open Source code for "Masked Autoencoders As Spatiotemporal Learners"

moses

Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

purdue-ece-562-database-management-fall-2017

Taught by Arif Ghafoor

purdue-ece-661-computer-vision-fall-2018

Taught by Avinash Kak

purdue-ma-598-neural-network

pytorch-vq-vae

PyTorch implementation of VQ-VAE by Aäron van den Oord et al.

pytorch-vqvae

Vector Quantized VAEs - PyTorch Implementation

shiarthur03

防走丢

stablediffusion

High-Resolution Image Synthesis with Latent Diffusion Models

torch-fidelity

High-fidelity performance metrics for generative models in PyTorch

transformer

Transformer: PyTorch Implementation of "Attention Is All You Need"

uncertainty_guided_optimization

Official repository for the paper "Improving black-box optimization in VAE latent space using decoder uncertainty" (Pascal Notin, José Miguel Hernández-Lobato, Yarin Gal)

uniformtransformationmodule

This respository is the experimental code of the UT module.

vit-pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

vq-diffusion

Official implementation of VQ-Diffusion

vq-vae-2-pytorch

Implementation of Generating Diverse High-Fidelity Images with VQ-VAE-2 in PyTorch