PyTorch Implementations For A Series Of Deep Learning-Based Recommendation Models
-
Updated
Nov 21, 2022 - Python
PyTorch Implementations For A Series Of Deep Learning-Based Recommendation Models
Autoencoders for Link Prediction and Semi-Supervised Node Classification (DSAA 2018)
A tensorflow.keras generative neural network for de novo drug design, first-authored in Nature Machine Intelligence while working at AstraZeneca.
Codes and Templates from the SuperDataScience Course
Automatic feature engineering using deep learning and Bayesian inference using TensorFlow.
Compressive Autoencoder.
Data and code related to the paper "ADAGE-Based Integration of Publicly Available Pseudomonas aeruginosa..." Jie Tan, et al · mSystems · 2016
Collection of operational time series ML models and tools
The code for the MaD TwinNet. Demo page:
Auto Encoders in PyTorch
An attempt to improve pix2code through pretrained autoencoders
Gradient Origin Networks - a new type of generative model that is able to quickly learn a latent representation without an encoder
Network-to-Network Translation with Conditional Invertible Neural Networks
🧶 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
Pytorch implementation of contractive autoencoder on MNIST dataset
Stacked Denoising AutoEncoder based on TensorFlow
Tensorflow implementation of "Transforming Autoencoders" (Proposed by G.E.Hinton, et al.)
Package for Multimodal Autoencoders in TensorFlow / Keras
Built a Movie Recommendation System using AutoEncoders.It was built using MovieLens Dataset
Add a description, image, and links to the autoencoders topic page so that developers can more easily learn about it.
To associate your repository with the autoencoders topic, visit your repo's landing page and select "manage topics."