PyTorch Implementations For A Series Of Deep Learning-Based Recommendation Models

Autoencoders for Link Prediction and Semi-Supervised Node Classification (DSAA 2018)

Network-to-Network Translation with Conditional Invertible Neural Networks

A tensorflow.keras generative neural network for de novo drug design, first-authored in Nature Machine Intelligence while working at AstraZeneca.

Compressive Autoencoder.

Collection of operational time series ML models and tools

Gradient Origin Networks - a new type of generative model that is able to quickly learn a latent representation without an encoder

🧶 Modular VAE disentanglement framework for python built with PyTorch Lightning ▸ Including metrics and datasets ▸ With strongly supervised, weakly supervised and unsupervised methods ▸ Easily configured and run with Hydra config ▸ Inspired by disentanglement_lib

The code for the MaD TwinNet. Demo page:

[ICCV 2023 Oral] Official Implementation of "Denoising Diffusion Autoencoders are Unified Self-supervised Learners"

Language Quantized AutoEncoders

Automatic feature engineering using deep learning and Bayesian inference using TensorFlow.

Auto Encoders in PyTorch

Data and code related to the paper "ADAGE-Based Integration of Publicly Available Pseudomonas aeruginosa..." Jie Tan, et al · mSystems · 2016

Tensorflow implementation of "Transforming Autoencoders" (Proposed by G.E.Hinton, et al.)

Implementation of autoencoders in PyTorch

Pytorch implementation of contractive autoencoder on MNIST dataset

Official Tensorflow implementation of the paper "Y-Autoencoders: disentangling latent representations via sequential-encoding", Pattern Recognition Letters (2020)

COALA: Co-Aligned Autoencoders for Learning Semantically Enriched Audio Representations

AutoKoopman - automated Koopman operator methods for data-driven dynamical systems analysis and control.