StephenCurry7

EDTalk

[ECCV 2024 Oral] EDTalk - Official PyTorch Implementation

DeepCTR-Torch

【PyTorch】Easy-to-use,Modular and Extendible package of deep-learning based CTR models.

FuxiCTR

A configurable, tunable, and reproducible library for CTR prediction https://fuxictr.github.io

argmax_flows

Code for paper "Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions"

PyTorchDiscreteFlows

Discrete Normalizing Flows implemented in PyTorch

pytorch-normalizing-flows

Normalizing flows in PyTorch. Current intended use is education not production.

made-pytorch

A PyTorch implementation of MADE (Masked Autoencoder Distribution Estimation)

NAF

Experiments for the Neural Autoregressive Flows paper

maf

PyTorch implementation of the Masked Autoregressive Flow

NNCME

maf

Masked Autoregressive Flow

DeepCTR

Easy-to-use,Modular and Extendible package of deep-learning based CTR models .

x-deeplearning

An industrial deep learning framework for high-dimension sparse data

dien

DeepInterestNetwork

DSIN

Code for the IJCAI'19 paper "Deep Session Interest Network for Click-Through Rate Prediction"

Taobao-advertising_CTR

通过淘宝用户点击广告的数据集进行用户广告的点击预测，数据集共4个

ORIC

DiVAE

A package containing neural network architectures based on Variational Autoencoders (VAE) and Restricted Boltzmann Machines (RBM) for learning discrete latent structures.

disentanglement_lib

disentanglement_lib is an open-source library for research on learning disentangled representations.

FactorVAE

Pytorch implementation of FactorVAE proposed in Disentangling by Factorising(http://arxiv.org/abs/1802.05983)

DEAR

Disentangled gEnerative cAusal Representation (DEAR)

trustworthyAI

Trustworthy AI related projects

ELBD-Efficient-score-algorithm-for-feature-selection-on-latent-variables-of-VAE

ARAF

RIT

A simple implementation of Random Intersection Trees

scikit-feature

open-source feature selection repository in python

pytorch-mnist-VAE

Awesome-VAEs

A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.

VAE-PyTorch

Variational Autoencoders trained on the SVHN and FashionMNIST data-sets implemented in PyTorch