Alternatives and similar repositories for aboleth:

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

• RuiShu / nn-bayesian-optimization
  We use a modified neural network instead of Gaussian process for Bayesian optimization.
  ☆108Updated 7 years ago
• jameshensman / VFF
  Variational Fourier Features
  ☆84Updated 3 years ago
• HIPS / Probabilistic-Backpropagation
  Implementation in C and Theano of the method Probabilistic Backpropagation for scalable Bayesian inference in deep neural networks.
  ☆192Updated 6 years ago
• alshedivat / keras-gp
  Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.
  ☆249Updated 7 months ago
• markvdw / convgp
  Convolutional Gaussian processes based on GPflow.
  ☆96Updated 7 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
• yaringal / DropoutUncertaintyCaffeModels
  Dropout As A Bayesian Approximation: Code
  ☆201Updated 9 years ago
• stepelu / ptstat
  Probabilistic Programming and Statistical Inference in PyTorch
  ☆109Updated 7 years ago
• yaringal / DropoutUncertaintyDemos
  What My Deep Model Doesn't Know...
  ☆117Updated 9 years ago
• powerscorinne / GLRM
  ☆98Updated 7 years ago
• mwhoffman / pybo
  Python package for modular Bayesian optimization
  ☆134Updated 4 years ago
• casperkaae / parmesan
  Variational and semi-supervised neural network toppings for Lasagne
  ☆208Updated 8 years ago
• arogozhnikov / infiniteboost
  InfiniteBoost: building infinite ensembles with gradient descent
  ☆184Updated 6 years ago
• RobRomijnders / weight_uncertainty
  Implementing Bayes by Backprop
  ☆183Updated 5 years ago
• edwardlib / papers
  Edward content including papers, posters, and talks
  ☆91Updated 4 years ago
• AI-DI / Brancher
  A user-centered Python package for differentiable probabilistic inference
  ☆202Updated 4 years ago
• HIPS / hypergrad
  Exploring differentiation with respect to hyperparameters
  ☆294Updated 9 years ago
• edwardlib / observations
  Tools for loading standard data sets in machine learning
  ☆203Updated 2 years ago
• yaringal / HeteroscedasticDropoutUncertainty
  Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.
  ☆140Updated 9 years ago
• mirceamironenco / BayesianRecurrentNN
  Implementation of Bayesian Recurrent Neural Networks by Fortunato et. al
  ☆218Updated 6 years ago
• yaringal / ConcreteDropout
  Code for Concrete Dropout as presented in https://arxiv.org/abs/1705.07832
  ☆249Updated 6 years ago
• martius-lab / l4-optimizer
  Code for paper "L4: Practical loss-based stepsize adaptation for deep learning"
  ☆125Updated 5 years ago
• brain-research / l2hmc
  TensorFlow implementation for training MCMC samplers from the paper: Generalizing Hamiltonian Monte Carlo with Neural Network
  ☆182Updated 6 years ago
• tqchen / ML-SGHMC
  Experiment code for Stochastic Gradient Hamiltonian Monte Carlo
  ☆105Updated 7 years ago
• bstriner / bayesian_dense
  Bayesian Weight Uncertainty Dense Layer for Keras
  ☆48Updated 8 years ago
• automl / HPOlib
  HPOlib is a hyperparameter optimization library. It provides a common interface to three state of the art hyperparameter optimization pac…
  ☆166Updated 6 years ago
• AMLab-Amsterdam / SEVDL_MGP
  Example implementation of the Bayesian neural network in "Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteri…
  ☆30Updated 4 years ago
• tabacof / bayesian-nn-uncertainty
  Classification uncertainty using Bayesian neural networks
  ☆33Updated 8 years ago
• blei-lab / deep-exponential-families
  Deep exponential families (DEFs)
  ☆55Updated 7 years ago