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)
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
[ICCV 2023 Oral] Official Implementation of "Denoising Diffusion Autoencoders are Unified Self-supervised Learners"
🧶 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:
Language Quantized AutoEncoders
Efficient World Models with Context-Aware Tokenization. ICML 2024
Official PyTorch Implementation of "Diffusion Autoencoders are Scalable Image Tokenizers"
Automatic feature engineering using deep learning and Bayesian inference using TensorFlow.
AutoKoopman - automated Koopman operator methods for data-driven dynamical systems analysis and control.
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.)
Pytorch implementation of various autoencoders (contractive, denoising, convolutional, randomized)
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."