Link PredictionRepresentation learning for link prediction within social networks
Pytorch VaeA Variational Autoencoder (VAE) implemented in PyTorch
DeepinfomaxpytorchLearning deep representations by mutual information estimation and maximization
Deep white balanceReference code for the paper: Deep White-Balance Editing, CVPR 2020 (Oral). Our method is a deep learning multi-task framework for white-balance editing.
SequiturLibrary of autoencoders for sequential data
DanmfA sparsity aware implementation of "Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection" (CIKM 2018).
Motion SenseMotionSense Dataset for Human Activity and Attribute Recognition ( time-series data generated by smartphone's sensors: accelerometer and gyroscope)
Kitnet PyKitNET is a lightweight online anomaly detection algorithm, which uses an ensemble of autoencoders.
ShapeganGenerative Adversarial Networks and Autoencoders for 3D Shapes
SplitbrainautoSplit-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction. In CVPR, 2017.
KateCode & data accompanying the KDD 2017 paper "KATE: K-Competitive Autoencoder for Text"
Pt DecPyTorch implementation of DEC (Deep Embedding Clustering)
TybaltTraining and evaluating a variational autoencoder for pan-cancer gene expression data
Srl ZooState Representation Learning (SRL) zoo with PyTorch - Part of S-RL Toolbox
DeeptimeDeep learning meets molecular dynamics.
Rectorchrectorch is a pytorch-based framework for state-of-the-art top-N recommendation
CalcConvolutional Autoencoder for Loop Closure
Pytorch cppDeep Learning sample programs using PyTorch in C++
GpndGenerative Probabilistic Novelty Detection with Adversarial Autoencoders
DeepaiDetection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.
Repo 2016R, Python and Mathematica Codes in Machine Learning, Deep Learning, Artificial Intelligence, NLP and Geolocation
SdcnStructural Deep Clustering Network
SmrtHandle class imbalance intelligently by using variational auto-encoders to generate synthetic observations of your minority class.
SegmentationTensorflow implementation : U-net and FCN with global convolution
DeepdepthdenoisingThis repo includes the source code of the fully convolutional depth denoising model presented in https://arxiv.org/pdf/1909.01193.pdf (ICCV19)
Pytorch sac aePyTorch implementation of Soft Actor-Critic + Autoencoder(SAC+AE)
Niftynet[unmaintained] An open-source convolutional neural networks platform for research in medical image analysis and image-guided therapy
Sdne KerasKeras implementation of Structural Deep Network Embedding, KDD 2016
AialphaUse unsupervised and supervised learning to predict stocks
Pt SdaePyTorch implementation of SDAE (Stacked Denoising AutoEncoder)
CodeslamImplementation of CodeSLAM — Learning a Compact, Optimisable Representation for Dense Visual SLAM paper (https://arxiv.org/pdf/1804.00874.pdf)
Repo 2017Python codes in Machine Learning, NLP, Deep Learning and Reinforcement Learning with Keras and Theano
Collaborative Deep Learning For Recommender SystemsThe hybrid model combining stacked denoising autoencoder with matrix factorization is applied, to predict the customer purchase behavior in the future month according to the purchase history and user information in the Santander dataset.
Basic nns in frameworksseveral basic neural networks[mlp, autoencoder, CNNs, recurrentNN, recursiveNN] implements under several NN frameworks[ tensorflow, pytorch, theano, keras]
RecoderLarge scale training of factorization models for Collaborative Filtering with PyTorch
Rnn VaeVariational Autoencoder with Recurrent Neural Network based on Google DeepMind's "DRAW: A Recurrent Neural Network For Image Generation"
Concise Ipython Notebooks For Deep LearningIpython Notebooks for solving problems like classification, segmentation, generation using latest Deep learning algorithms on different publicly available text and image data-sets.