Alternatives and similar repositories for kvae:

Users that are interested in kvae are comparing it to the libraries listed below

• boschresearch / PR-SSM
  Python implementation of the PR-SSM.
  ☆51Updated 6 years ago
• clinicalml / structuredinference
  Structured Inference Networks for Nonlinear State Space Models
  ☆267Updated 7 years ago
• blei-lab / variational-smc
  Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)
  ☆65Updated 5 years ago
• microsoft / deterministic-variational-inference
  Sample code for running deterministic variational inference to train Bayesian neural networks
  ☆100Updated 6 years ago
• marcofraccaro / srnn
  Code for "Sequential Neural Models with Stochastic Layers"
  ☆115Updated 8 years ago
• mattjj / pyslds
  ☆94Updated 6 years ago
• sheqi / GP-RNN_UAI2019
  Implementaion of Gaussian Process Recurrent Neural Networks developed in "Neural Dynamics Discovery via Gaussian Process Recurrent Neura…
  ☆40Updated 2 years ago
• alshedivat / keras-gp
  Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.
  ☆249Updated 7 months ago
• emited / VariationalRecurrentNeuralNetwork
  Pytorch implementation of the Variational Recurrent Neural Network (VRNN).
  ☆285Updated 3 years ago
• geniki / neural-processes
  PyTorch implementation of Neural Processes
  ☆88Updated 6 years ago
• LCAS / RKN
  ☆47Updated 2 years ago
• emtiyaz / vmp-for-svae
  Variational Message Passing for Structured VAE (Code for ICLR 2018 paper)
  ☆44Updated 6 years ago
• markvdw / convgp
  Convolutional Gaussian processes based on GPflow.
  ☆96Updated 7 years ago
• axelbrando / Mixture-Density-Networks-for-distribution-and-uncertainty-estimation
  A generic Mixture Density Networks (MDN) implementation for distribution and uncertainty estimation by using Keras (TensorFlow)
  ☆346Updated 7 years ago
• ssydasheng / FBNN
  Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)
  ☆81Updated 4 years ago
• yaringal / HeteroscedasticDropoutUncertainty
  Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.
  ☆140Updated 9 years ago
• UCL-SML / Doubly-Stochastic-DGP
  Deep Gaussian Processes with Doubly Stochastic Variational Inference
  ☆149Updated 6 years ago
• ssydasheng / Neural-Kernel-Network
  Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326
  ☆71Updated 6 years ago
• brain-research / ncp
  Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors
  ☆62Updated 4 years ago
• msmbuilder / vde
  Variational Autoencoder for Dimensionality Reduction of Time-Series
  ☆187Updated 2 years ago
• kasparmartens / NeuralProcesses
  Neural Processes implementation for 1D regression
  ☆65Updated 6 years ago
• t-vi / candlegp
  Gaussian Processes in Pytorch
  ☆75Updated 5 years ago
• hughsalimbeni / bayesian_benchmarks
  A community repository for benchmarking Bayesian methods
  ☆110Updated 3 years ago
• sksq96 / pytorch-mdn
  Mixture Density Networks (MDN) implemented in PyTorch
  ☆66Updated 4 years ago
• riannevdberg / sylvester-flows
  ☆179Updated 5 years ago
• cics-nd / gptorch
  Gaussian processes with PyTorch
  ☆30Updated 3 years ago
• mattjj / pylds
  some tools for gaussian linear dynamical systems
  ☆90Updated 6 years ago
• otokonoko8 / deep-Bayesian-nonparametrics-papers
  The collection of papers about combining deep learning and Bayesian nonparametrics
  ☆120Updated 5 years ago
• freelunchtheorem / Conditional_Density_Estimation
  Package implementing various parametric and nonparametric methods for conditional density estimation
  ☆193Updated last year
• amirgholami / anode
  [IJCAI'19, NeurIPS'19] Anode: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs
  ☆104Updated 4 years ago