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NeurecNext RecSys Library
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AutoencodersTorch implementations of various types of autoencoders
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AialphaUse unsupervised and supervised learning to predict stocks
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Basic nns in frameworksseveral basic neural networks[mlp, autoencoder, CNNs, recurrentNN, recursiveNN] implements under several NN frameworks[ tensorflow, pytorch, theano, keras]
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SdcnStructural Deep Clustering Network
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Rnn VaeVariational Autoencoder with Recurrent Neural Network based on Google DeepMind's "DRAW: A Recurrent Neural Network For Image Generation"
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Rectorchrectorch is a pytorch-based framework for state-of-the-art top-N recommendation
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Ad examplesA collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.
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Pt DecPyTorch implementation of DEC (Deep Embedding Clustering)
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Generative ModelsAnnotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN
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Sdne KerasKeras implementation of Structural Deep Network Embedding, KDD 2016
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Deepsvg[NeurIPS 2020] Official code for the paper "DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation". Includes a PyTorch library for deep learning with SVG data.
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GpndGenerative Probabilistic Novelty Detection with Adversarial Autoencoders
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MolencoderMolecular AutoEncoder in PyTorch
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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.
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Cifar-AutoencoderA look at some simple autoencoders for the Cifar10 dataset, including a denoising autoencoder. Python code included.
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Repo 2016R, Python and Mathematica Codes in Machine Learning, Deep Learning, Artificial Intelligence, NLP and Geolocation
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DeeptimeDeep learning meets molecular dynamics.
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RecoderLarge scale training of factorization models for Collaborative Filtering with PyTorch
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SmrtHandle class imbalance intelligently by using variational auto-encoders to generate synthetic observations of your minority class.
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KateCode & data accompanying the KDD 2017 paper "KATE: K-Competitive Autoencoder for Text"
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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.
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CalcConvolutional Autoencoder for Loop Closure
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Keras Idiomatic ProgrammerBooks, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework
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DeepdepthdenoisingThis repo includes the source code of the fully convolutional depth denoising model presented in https://arxiv.org/pdf/1909.01193.pdf (ICCV19)
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Tensorflow BookAccompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
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Tensorflow Mnist CvaeTensorflow implementation of conditional variational auto-encoder for MNIST
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PyodA Python Toolbox for Scalable Outlier Detection (Anomaly Detection)
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Niftynet[unmaintained] An open-source convolutional neural networks platform for research in medical image analysis and image-guided therapy
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Tensorflow Mnist VaeTensorflow implementation of variational auto-encoder for MNIST
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Pytorch cppDeep Learning sample programs using PyTorch in C++
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Tensorflow TutorialTensorflow tutorial from basic to hard, 莫烦Python 中文AI教学
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Image similarityPyTorch Blog Post On Image Similarity Search
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ZhihuThis repo contains the source code in my personal column (https://zhuanlan.zhihu.com/zhaoyeyu), implemented using Python 3.6. Including Natural Language Processing and Computer Vision projects, such as text generation, machine translation, deep convolution GAN and other actual combat code.
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TybaltTraining and evaluating a variational autoencoder for pan-cancer gene expression data
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Alae[CVPR2020] Adversarial Latent Autoencoders
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Pt SdaePyTorch implementation of SDAE (Stacked Denoising AutoEncoder)
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Noise2Noise-audio denoising without clean training dataSource code for the paper titled "Speech Denoising without Clean Training Data: a Noise2Noise Approach". Paper accepted at the INTERSPEECH 2021 conference. This paper tackles the problem of the heavy dependence of clean speech data required by deep learning based audio denoising methods by showing that it is possible to train deep speech denoisi…
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CodeslamImplementation of CodeSLAM — Learning a Compact, Optimisable Representation for Dense Visual SLAM paper (https://arxiv.org/pdf/1804.00874.pdf)
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SplitbrainautoSplit-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction. In CVPR, 2017.
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Srl ZooState Representation Learning (SRL) zoo with PyTorch - Part of S-RL Toolbox
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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.
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Repo 2017Python codes in Machine Learning, NLP, Deep Learning and Reinforcement Learning with Keras and Theano
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