Alternatives and similar repositories for aboleth:

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

• arogozhnikov / infiniteboost
  InfiniteBoost: building infinite ensembles with gradient descent
  ☆184Updated 6 years ago
• stepelu / ptstat
  Probabilistic Programming and Statistical Inference in PyTorch
  ☆110Updated 7 years ago
• alshedivat / keras-gp
  Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.
  ☆249Updated 5 months ago
• bstriner / bayesian_dense
  Bayesian Weight Uncertainty Dense Layer for Keras
  ☆48Updated 8 years ago
• martius-lab / l4-optimizer
  Code for paper "L4: Practical loss-based stepsize adaptation for deep learning"
  ☆124Updated 5 years ago
• RuiShu / nn-bayesian-optimization
  We use a modified neural network instead of Gaussian process for Bayesian optimization.
  ☆108Updated 7 years ago
• mblondel / fenchel-young-losses
  Probabilistic classification in PyTorch/TensorFlow/scikit-learn with Fenchel-Young losses
  ☆183Updated last year
• AI-DI / Brancher
  A user-centered Python package for differentiable probabilistic inference
  ☆203Updated 4 years ago
• automl / labwatch
  An extension to Sacred for automated hyperparameter optimization.
  ☆59Updated 6 years ago
• edwardlib / papers
  Edward content including papers, posters, and talks
  ☆90Updated 4 years ago
• powerscorinne / GLRM
  ☆97Updated 6 years ago
• jameshensman / VFF
  Variational Fourier Features
  ☆83Updated 3 years ago
• edwardlib / observations
  Tools for loading standard data sets in machine learning
  ☆203Updated 2 years ago
• pierreablin / autoptim
  Automatic differentiation + optimization
  ☆104Updated 5 years ago
• drckf / paysage
  Unsupervised learning and generative models in python/pytorch.
  ☆119Updated 2 years ago
• HIPS / Probabilistic-Backpropagation
  Implementation in C and Theano of the method Probabilistic Backpropagation for scalable Bayesian inference in deep neural networks.
  ☆192Updated 5 years ago
• ferrine / gelato
  Bayesian dessert for Lasagne
  ☆84Updated 7 years ago
• yaringal / HeteroscedasticDropoutUncertainty
  Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.
  ☆140Updated 8 years ago
• RobRomijnders / weight_uncertainty
  Implementing Bayes by Backprop
  ☆183Updated 5 years ago
• markvdw / convgp
  Convolutional Gaussian processes based on GPflow.
  ☆95Updated 7 years ago
• mirceamironenco / BayesianRecurrentNN
  Implementation of Bayesian Recurrent Neural Networks by Fortunato et. al
  ☆217Updated 6 years ago
• t-vi / candlegp
  Gaussian Processes in Pytorch
  ☆75Updated 4 years ago
• ronojoy / pylearn
  Bayesian machine learning in Python
  ☆76Updated 9 years ago
• mwhoffman / pybo
  Python package for modular Bayesian optimization
  ☆134Updated 4 years ago
• vene / pyowl
  Ordered Weighted L1 regularization for classification and regression in Python
  ☆52Updated 6 years ago
• HIPS / hypergrad
  Exploring differentiation with respect to hyperparameters
  ☆295Updated 9 years ago
• yaringal / DropoutUncertaintyCaffeModels
  Dropout As A Bayesian Approximation: Code
  ☆201Updated 9 years ago
• mauriziofilippone / deep_gp_random_features
  ☆40Updated 5 years ago
• SheffieldML / notebook
  Collection of jupyter notebooks for demonstrating software.
  ☆165Updated last year
• automl / HPOlib1.5
  ☆69Updated 4 years ago