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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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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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NeurecNext RecSys Library
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GpndGenerative Probabilistic Novelty Detection with Adversarial Autoencoders
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Rectorchrectorch is a pytorch-based framework for state-of-the-art top-N recommendation
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vae-concreteKeras implementation of a Variational Auto Encoder with a Concrete Latent Distribution
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Cada Vae PytorchOfficial implementation of the paper "Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders" (CVPR 2019)
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BagelIPCCC 2018: Robust and Unsupervised KPI Anomaly Detection Based on Conditional Variational Autoencoder
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pyroVEDInvariant representation learning from imaging and spectral data
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probabilistic nlgTensorflow Implementation of Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation (NAACL 2019).
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Pytorch cppDeep Learning sample programs using PyTorch in C++
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Srl ZooState Representation Learning (SRL) zoo with PyTorch - Part of S-RL Toolbox
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Fun-with-MNISTPlaying with MNIST. Machine Learning. Generative Models.
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continuous BernoulliThere are C language computer programs about the simulator, transformation, and test statistic of continuous Bernoulli distribution. More than that, the book contains continuous Binomial distribution and continuous Trinomial distribution.
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catseyeNeural network library written in C and Javascript
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vae-pytorchAE and VAE Playground in PyTorch
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MIDI-VAENo description or website provided.
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playing with vaeComparing FC VAE / FCN VAE / PCA / UMAP on MNIST / FMNIST
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Awesome VaesA curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.
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