Alternatives and similar repositories for DPMM

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

• YoungseogChung / calibrated-quantile-uq
  Repository for Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification (NeurIPS 2024)
  ☆44Updated 10 months ago
• abietti / dpmm
  Variational inference for Dirichlet process mixture models with multinomial mixture components.
  ☆35Updated 11 years ago
• wjmaddox / drbayes
  Code Repo for "Subspace Inference for Bayesian Deep Learning"
  ☆83Updated last year
• bobby-he / bayesian-ntk
  Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'
  ☆26Updated 4 years ago
• TeaPearce / Bayesian_NN_Ensembles
  AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'
  ☆89Updated 5 years ago
• skolouri / swgmm
  Sliced Wasserstein Distance for Learning Gaussian Mixture Models
  ☆66Updated 2 years ago
• lsgos / uncertainty-adversarial-paper
  Code for the paper 'Understanding Measures of Uncertainty for Adversarial Example Detection'
  ☆61Updated 7 years ago
• mcusi / tf_dpgmm
  Variational inference in Dirichlet process Gaussian mixture model (tensorflow implementation)
  ☆13Updated 7 years ago
• sudiptodip15 / CCMI
  Classifier based mutual information, conditional mutual information estimation; conditional independence testing
  ☆35Updated 6 years ago
• cambridge-mlg / cnn-gp
  Pytorch version of "Deep Convolutional Networks as shallow Gaussian Processes" by Adrià Garriga-Alonso, Carl Rasmussen and Laurence Aitch…
  ☆32Updated 5 years ago
• Piyushi-0 / ACE
  Code for our ICML '19 paper: Neural Network Attributions: A Causal Perspective.
  ☆51Updated 3 years ago
• IBM / wasserstein-barycenters
  Code for our ICLR19 paper "Wasserstein Barycenters for Model Ensembling", Pierre Dognin, Igor Melnyk, Youssef Mroueh, Jarret Ross, Cicero…
  ☆22Updated 5 years ago
• kekeblom / DeepCGP
  Deep convolutional gaussian processes.
  ☆81Updated 6 years ago
• geosada / LVAE
  LVAE: Ladder Variational Auto-Encoders (NIPS 2016) with TensorFlow.
  ☆16Updated 7 years ago
• JanRocketMan / regression-prior-networks
  An official PyTorch implementation of "Regression Prior Networks" for effective runtime uncertainty estimation.
  ☆36Updated 4 years ago
• mingzhang-yin / SIVI
  A variational inference method with accurate uncertainty estimation. It uses a new semi-implicit variational family built on neural netwo…
  ☆54Updated last year
• microsoft / deterministic-variational-inference
  Sample code for running deterministic variational inference to train Bayesian neural networks
  ☆100Updated 7 years ago
• steveli / partial-encoder-decoder
  An encoder-decoder framework for learning from incomplete data
  ☆45Updated 2 years ago
• namkoong-lab / robustopt
  ☆43Updated 7 years ago
• facebookresearch / SingleModelUncertainty
  Learning error bars for neural network predictions
  ☆71Updated 5 years ago
• mrojascarulla / causal_transfer_learning
  ☆32Updated 7 years ago
• francoispierrepaty / SubspaceRobustWasserstein
  Source code for the ICML2019 paper "Subspace Robust Wasserstein Distances"
  ☆29Updated 6 years ago
• dtak / ocbnn-public
  General purpose library for BNNs, and implementation of OC-BNNs in our 2020 NeurIPS paper.
  ☆38Updated 3 years ago
• yorkerlin / VB-MixEF
  Code for ICML 2019 paper on "Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations"
  ☆19Updated 4 years ago
• noahgolmant / SGLD
  Stochastic Gradient Langevin Dynamics for Bayesian learning
  ☆35Updated 3 years ago
• dilinwang820 / nonlinear_svgd
  Nonlinear SVGD for Learning Diversified Mixture Models
  ☆13Updated 6 years ago
• likang7 / variational-dpgmm
  Variational Dirichlet Process Gaussian Mixture Models
  ☆29Updated 10 years ago
• JonathanWenger / pycalib
  Non-Parametric Calibration for Classification (AISTATS 2020)
  ☆19Updated 3 years ago
• pamattei / miwae
  Implementation of the MIWAE method for deep generative modelling of incomplete data sets.
  ☆41Updated last year
• ColinQiyangLi / LConvNet
  Implementation of Methods Proposed in Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks (NeurIPS 2019)
  ☆35Updated 5 years ago