Alternatives and similar repositories for GMM_DAE

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

• vios-s / DiffAN
  Diffusion Models for Causal Discovery
  ☆92Updated 2 years ago
• facebookresearch / CausalRepID
  Experiments to reproduce results in Interventional Causal Representation Learning.
  ☆28Updated 3 years ago
• vdutor / neural-diffusion-processes
  Neural Diffusion Processes
  ☆82Updated last year
• crisbodnar / ndp
  Official code for the ICLR 2021 paper Neural ODE Processes
  ☆76Updated 3 years ago
• ilkhem / icebeem
  Code for ICE-BeeM paper - NeurIPS 2020
  ☆87Updated 4 years ago
• psanch21 / causal-flows
  ☆53Updated last year
• bpucla / latent-space-EBM-prior
  ☆37Updated 5 years ago
• namratadeka / circe
  Efficient Conditionally Invariant Representation Learning (ICLR 2023, Oral)
  ☆21Updated 3 years ago
• sharpenb / Posterior-Network
  Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts (Neurips 2020)
  ☆78Updated 3 years ago
• iamalexkorotin / Wasserstein2Barycenters
  PyTorch implementation of the paper "Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization" (ICLR 2021)
  ☆33Updated 3 years ago
• iamalexkorotin / Wasserstein2GenerativeNetworks
  PyTorch implementation of "Wasserstein-2 Generative Networks" (ICLR 2021)
  ☆66Updated 3 years ago
• tung-nd / TNP-pytorch
  Official implementation of Transformer Neural Processes
  ☆78Updated 3 years ago
• zengyi-li / NMCE-release
  Code for Neural Manifold Clustering and Embedding
  ☆62Updated 3 years ago
• volagold / nce
  Noise Contrastive Estimation (NCE) in PyTorch
  ☆32Updated 11 months ago
• ermongroup / f-dre
  Featurized Density Ratio Estimation
  ☆20Updated 4 years ago
• EmoryMLIP / OT-Flow
  PyTorch implementation of the OT-Flow approach in arXiv:2006.00104
  ☆57Updated last year
• tvayer / SGW
  Code for Sliced Gromov-Wasserstein
  ☆69Updated 6 years ago
• chrhenning / hypnettorch
  Package for working with hypernetworks in PyTorch.
  ☆132Updated 2 years ago
• vislearn / GIN
  Code for the paper "Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)" (2020)
  ☆33Updated 4 years ago
• xwshen51 / DEAR
  Disentangled gEnerative cAusal Representation (DEAR)
  ☆65Updated 3 years ago
• LituRout / OptimalTransportModeling
  Generative Modeling with Optimal Transport Maps - ICLR 2022
  ☆60Updated 3 years ago
• vislearn / IB-INN
  Code for the paper "Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification" (2020)
  ☆43Updated last year
• cianeastwood / qrm
  Quantile risk minimization
  ☆25Updated last year
• a7b23 / H-Divergence
  ☆22Updated 3 years ago
• ppope / dimensions
  Code for "The Intrinsic Dimension of Images and Its Impact on Learning" - ICLR 2021 Spotlight https://openreview.net/forum?id=XJk19XzGq2J
  ☆72Updated last year
• bobby-he / bayesian-ntk
  Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'
  ☆26Updated 4 years ago
• patrickrchao / DiffusionBasedCausalModels
  ☆31Updated 9 months ago
• siamakz / iVAE
  VAEs and nonlinear ICA: a unifying framework
  ☆50Updated 6 years ago
• fengliu90 / DK-for-TST
  This is the source code for Learning Deep Kernels for Non-Parametric Two-Sample Tests (ICML2020).
  ☆53Updated 4 years ago
• piomonti / carefl
  Code for "Causal autoregressive flows" - AISTATS, 2021
  ☆45Updated 4 years ago