probabilistic-learning / HI-VAE

Related projects ⓘ

Alternatives and complementary repositories for HI-VAE

• pamattei / miwae
  Implementation of the MIWAE method for deep generative modelling of incomplete data sets.
  ☆37Updated 8 months ago
• ratschlab / GP-VAE
  TensorFlow implementation for the GP-VAE model described in https://arxiv.org/abs/1907.04155
  ☆127Updated last year
• ioanabica / Counterfactual-Recurrent-Network
  Code for ICLR 2020 paper: "Estimating counterfactual treatment outcomes over time through adversarially balanced representations" by I. B…
  ☆56Updated 8 months ago
• xiaoshuai09 / Wasserstein-Learning-For-Point-Process
  learning point processes by means of optimal transport and wasserstein distance
  ☆54Updated 6 years ago
• rcamino / imputation-dgm
  Code for the paper "Improving Missing Data Imputation with Deep Generative Models"
  ☆32Updated 5 years ago
• diviyank / SAM
  Code for the Structural Agnostic Model (https://arxiv.org/abs/1803.04929)
  ☆53Updated 3 years ago
• steveli / partial-encoder-decoder
  An encoder-decoder framework for learning from incomplete data
  ☆45Updated last year
• steveli / misgan
  MisGAN: Learning from Incomplete Data with GANs
  ☆80Updated last year
• BorisMuzellec / MissingDataOT
  A Pytorch implementation of missing data imputation using optimal transport.
  ☆95Updated 3 years ago
• Harry24k / MIDA-pytorch
  PyTorch implementation of "MIDA: Multiple Imputation using Denoising Autoencoders"
  ☆26Updated 5 years ago
• sfme / RVAE_MixedTypes
  Repository for code release of paper "Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data" (AISTATS 2020)
  ☆50Updated 4 years ago
• GoudetOlivier / CGNN
  Replication code for the article "Learning Functional Causal Models with Generative Neural Networks"
  ☆98Updated 5 years ago
• sjblim / rmsn_nips_2018
  ☆37Updated 5 years ago
• zhiyongc / GRU-D
  Gated Recurrent Unit with a Decay mechanism for Multivariate Time Series with Missing Values
  ☆113Updated 5 years ago
• loeweX / AmortizedCausalDiscovery
  Code for the paper: Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data
  ☆201Updated 2 years ago
• kurowasan / GraN-DAG
  ☆88Updated last year
• jsyoon0823 / INVASE
  Codebase for INVASE: Instance-wise Variable Selection - 2019 ICLR
  ☆60Updated 4 years ago
• babylonhealth / neuralTPPs
  ☆28Updated last year
• sanatonek / time_series_explainability
  ☆60Updated 3 years ago
• ioanabica / Time-Series-Deconfounder
  Code for ICML 2020 paper: "Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders" by I. …
  ☆51Updated 3 years ago
• ignavierng / golem
  On the Role of Sparsity and DAG Constraints for Learning Linear DAGs
  ☆33Updated 3 years ago
• d909b / cxplain
  Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.
  ☆129Updated 4 years ago
• ratschlab / dpsom
  Code associated with ACM-CHIL 21 paper 'T-DPSOM - An Interpretable Clustering Method for Unsupervised Learning of Patient Health States'
  ☆66Updated 3 years ago
• ilkhem / iVAE
  VAEs and nonlinear ICA: a unifying framework
  ☆30Updated 4 years ago
• ratschlab / SOM-VAE
  TensorFlow implementation of the SOM-VAE model as described in https://arxiv.org/abs/1806.02199
  ☆190Updated last year
• crazysal / VariationalRNN
  A Recurrent Latent Variable Model for Sequential Data
  ☆24Updated 6 years ago
• ayaabdelsalam91 / TS-Interpretability-Benchmark
  ☆86Updated 3 years ago
• omitakahiro / NeuralNetworkPointProcess
  code for "Fully Neural Network based Model for General Temporal Point Processes"
  ☆60Updated 3 years ago
• MarkPKCollier / MissingDataVAEs
  ☆19Updated 4 years ago
• blei-lab / ars-reparameterization
  Source code for Naesseth et. al. "Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms" (2017)
  ☆39Updated 7 years ago