Implementation of MADE (Masked Autoencoder for Distribution Estimation) with chainer
-
Updated
Aug 24, 2018 - Python
Implementation of MADE (Masked Autoencoder for Distribution Estimation) with chainer
Implementation of Neural Processes with chainer
Deep generative models in a reinforcement learning framework.
An implementation of Restricted Boltzmann Machine in Pytorch
A Tensorflow-layer API Implementation of Deep Generative Models (MNIST Examples)
FontGenerator is an end-to-end system which generates handwritten characters with some font using conditional Generative Adversarial Network (C-GANs)
A TensorFlow implementation of "Sequence Modeling with Hierarchical Deep Generative Models with Dual Memory" (published in CIKM2017).
The wonderful and illustrative notes on Deep Learning take will take a person from Zero to Hero.
[AAAI20] TensorFlow implementation of the Collaborative Sampling in Generative Adversarial Networks
MIT 6.S191: Introduction to Deep Learning Labs from Zero to Hero.
Code for variable skipping ICML 2020 paper
State-of-the-art neural cardinality estimators for join queries
Different types of Generative Adversarial Networks and their applications, training workflow, etc.
Implementation of NeurIPS 20 paper: Latent Template Induction with Gumbel-CRFs
Jointly Learning Word and Metadata Embeddings: Latent Meaning Cells Applied to Clinical Acronym Expansion
PyTorch implementation of the paper "NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity." (NeurIPS 2020)
A StyleGAN based on NVIDIA's paper.
A Time-series Image Segmentation tool with Semi-Unsupervised Encoders.
Official implementation of CVPR2020 paper "Deep Generative Model for Robust Imbalance Classification"
Add a description, image, and links to the deep-generative-model topic page so that developers can more easily learn about it.
To associate your repository with the deep-generative-model topic, visit your repo's landing page and select "manage topics."