mirceamironenco / BayesianRecurrentNN
Implementation of Bayesian Recurrent Neural Networks by Fortunato et. al
☆217Updated 5 years ago
Related projects ⓘ
Alternatives and complementary repositories for BayesianRecurrentNN
- Code for the paper "A Theoretically Grounded Application of Dropout in Recurrent Neural Networks"☆376Updated 7 years ago
- Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.☆139Updated 8 years ago
- Tensorflow implementation for DilatedRNN☆346Updated 7 years ago
- This repository contains the code to reproduce the core results from the paper "Adversarial Variational Bayes: Unifying Variational Autoe…☆206Updated 6 years ago
- Python package facilitating the use of Bayesian Deep Learning methods with Variational Inference for PyTorch☆359Updated 5 years ago
- We use a modified neural network instead of Gaussian process for Bayesian optimization.☆107Updated 7 years ago
- Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.☆247Updated 3 months ago
- Deep generative models for semi-supervised learning.☆109Updated 8 years ago
- ☆62Updated 7 years ago
- Tools for loading standard data sets in machine learning☆202Updated 2 years ago
- A variational recurrent neural network implementation in tensorflow☆104Updated 6 years ago
- Tensorflow implementation of a Hierarchical and Multiscale RNN, described in https://arxiv.org/abs/1609.01704☆135Updated 7 years ago
- Code for "Sequential Neural Models with Stochastic Layers"☆114Updated 8 years ago
- Dilated RNNs in pytorch☆211Updated 5 years ago
- Implementing Bayes by Backprop☆182Updated 5 years ago
- Convolutional Gaussian processes based on GPflow.☆95Updated 7 years ago
- Dropout As A Bayesian Approximation: Code☆199Updated 9 years ago
- supercell☆190Updated 7 years ago
- Classification uncertainty using Bayesian neural networks☆33Updated 8 years ago
- Replication of Semi-Supervised Learning with Deep Generative Models☆98Updated 8 years ago
- Code for Concrete Dropout as presented in https://arxiv.org/abs/1705.07832☆246Updated 6 years ago
- Structured Inference Networks for Nonlinear State Space Models☆265Updated 7 years ago
- A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation☆127Updated 3 years ago
- TensorFlow implementation of Bayes-by-Backprop algorithm from "Weight Uncertainty in Neural Networks" paper☆51Updated 5 years ago
- Neural Processes implementation for 1D regression☆65Updated 5 years ago
- Batch normalized LSTM for tensorflow☆180Updated 8 years ago
- Experiments used in "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"☆554Updated 2 years ago
- Bayesian Weight Uncertainty Dense Layer for Keras☆48Updated 7 years ago
- Gaussian Processes in Pytorch☆74Updated 4 years ago
- Example implementation of the Bayesian neural network in "Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteri…☆31Updated 4 years ago