Alternatives and similar repositories for aboleth

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

• alshedivat / keras-gp
  Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.
  ☆251Updated last year
• HIPS / Probabilistic-Backpropagation
  Implementation in C and Theano of the method Probabilistic Backpropagation for scalable Bayesian inference in deep neural networks.
  ☆191Updated 6 years ago
• stepelu / ptstat
  Probabilistic Programming and Statistical Inference in PyTorch
  ☆111Updated 8 years ago
• RuiShu / nn-bayesian-optimization
  We use a modified neural network instead of Gaussian process for Bayesian optimization.
  ☆108Updated 8 years ago
• arogozhnikov / infiniteboost
  InfiniteBoost: building infinite ensembles with gradient descent
  ☆183Updated 7 years ago
• HIPS / hypergrad
  Exploring differentiation with respect to hyperparameters
  ☆296Updated 10 years ago
• martius-lab / l4-optimizer
  Code for paper "L4: Practical loss-based stepsize adaptation for deep learning"
  ☆124Updated 6 years ago
• edwardlib / papers
  Edward content including papers, posters, and talks
  ☆92Updated 5 years ago
• powerscorinne / GLRM
  ☆99Updated 7 years ago
• yaringal / DropoutUncertaintyCaffeModels
  Dropout As A Bayesian Approximation: Code
  ☆204Updated 10 years ago
• edwardlib / observations
  Tools for loading standard data sets in machine learning
  ☆206Updated 3 years ago
• bstriner / bayesian_dense
  Bayesian Weight Uncertainty Dense Layer for Keras
  ☆47Updated 9 years ago
• automl / labwatch
  An extension to Sacred for automated hyperparameter optimization.
  ☆59Updated 7 years ago
• yaringal / HeteroscedasticDropoutUncertainty
  Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.
  ☆141Updated 9 years ago
• markvdw / convgp
  Convolutional Gaussian processes based on GPflow.
  ☆95Updated 8 years ago
• jameshensman / VFF
  Variational Fourier Features
  ☆87Updated 4 years ago
• RobRomijnders / weight_uncertainty
  Implementing Bayes by Backprop
  ☆184Updated 6 years ago
• mirceamironenco / BayesianRecurrentNN
  Implementation of Bayesian Recurrent Neural Networks by Fortunato et. al
  ☆220Updated 7 years ago
• vene / pyowl
  Ordered Weighted L1 regularization for classification and regression in Python
  ☆52Updated 7 years ago
• pierreablin / autoptim
  Automatic differentiation + optimization
  ☆101Updated 6 years ago
• brain-research / l2hmc
  TensorFlow implementation for training MCMC samplers from the paper: Generalizing Hamiltonian Monte Carlo with Neural Network
  ☆184Updated 7 years ago
• mwhoffman / pybo
  Python package for modular Bayesian optimization
  ☆136Updated 5 years ago
• yaringal / DropoutUncertaintyDemos
  What My Deep Model Doesn't Know...
  ☆118Updated 10 years ago
• SheffieldML / collab
  Collaborative filtering with the GP-LVM
  ☆25Updated 10 years ago
• davmre / elbow
  Flexible Bayesian inference using TensorFlow
  ☆142Updated 8 years ago
• casmls / casmls.github.io
  Columbia Advanced Machine Learning Seminar
  ☆24Updated 7 years ago
• Bihaqo / exp-machines
  Exponential Machines implementation
  ☆43Updated 11 months ago
• ferrine / gelato
  Bayesian dessert for Lasagne
  ☆83Updated 8 years ago
• casperkaae / parmesan
  Variational and semi-supervised neural network toppings for Lasagne
  ☆210Updated 9 years ago
• yaringal / BayesianRNN
  Code for the paper "A Theoretically Grounded Application of Dropout in Recurrent Neural Networks"
  ☆380Updated 9 years ago