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.
  ☆249Updated 10 months ago
• 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
  ☆85Updated 3 years ago
• edwardlib / papers
  Edward content including papers, posters, and talks
  ☆91Updated 4 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
• 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
• yaringal / DropoutUncertaintyCaffeModels
  Dropout As A Bayesian Approximation: Code
  ☆202Updated 9 years ago
• mirceamironenco / BayesianRecurrentNN
  Implementation of Bayesian Recurrent Neural Networks by Fortunato et. al
  ☆218Updated 6 years ago
• powerscorinne / GLRM
  ☆98Updated 7 years ago
• arogozhnikov / infiniteboost
  InfiniteBoost: building infinite ensembles with gradient descent
  ☆184Updated 6 years ago
• yaringal / DropoutUncertaintyDemos
  What My Deep Model Doesn't Know...
  ☆117Updated 9 years ago
• markvdw / convgp
  Convolutional Gaussian processes based on GPflow.
  ☆96Updated 7 years ago
• RobRomijnders / weight_uncertainty
  Implementing Bayes by Backprop
  ☆184Updated 6 years ago
• stepelu / ptstat
  Probabilistic Programming and Statistical Inference in PyTorch
  ☆110Updated 7 years ago
• mblondel / fenchel-young-losses
  Probabilistic classification in PyTorch/TensorFlow/scikit-learn with Fenchel-Young losses
  ☆185Updated last year
• martius-lab / l4-optimizer
  Code for paper "L4: Practical loss-based stepsize adaptation for deep learning"
  ☆124Updated 6 years ago
• yaringal / BayesianRNN
  Code for the paper "A Theoretically Grounded Application of Dropout in Recurrent Neural Networks"
  ☆378Updated 8 years ago
• t-vi / candlegp
  Gaussian Processes in Pytorch
  ☆75Updated 5 years ago
• YingzhenLi / Dropout_BBalpha
  Implementations of the ICML 2017 paper (with Yarin Gal)
  ☆38Updated 7 years ago
• mwhoffman / pybo
  Python package for modular Bayesian optimization
  ☆135Updated 4 years ago
• ctallec / pyvarinf
  Python package facilitating the use of Bayesian Deep Learning methods with Variational Inference for PyTorch
  ☆359Updated 5 years ago
• kasparmartens / NeuralProcesses
  Neural Processes implementation for 1D regression
  ☆65Updated 6 years ago
• vene / pyowl
  Ordered Weighted L1 regularization for classification and regression in Python
  ☆52Updated 6 years ago
• tabacof / bayesian-nn-uncertainty
  Classification uncertainty using Bayesian neural networks
  ☆33Updated 8 years ago
• SheffieldML / notebook
  Collection of jupyter notebooks for demonstrating software.
  ☆167Updated 2 years ago
• automl / labwatch
  An extension to Sacred for automated hyperparameter optimization.
  ☆59Updated 7 years ago
• davmre / elbow
  Flexible Bayesian inference using TensorFlow
  ☆143Updated 8 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
• AI-DI / Brancher
  A user-centered Python package for differentiable probabilistic inference
  ☆202Updated 4 years ago