melihkandemir / atldgp
Asymmetric Transfer Learning with Deep Gaussian Processes
☆18Updated 9 years ago
Related projects: ⓘ
- Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models.☆43Updated 10 years ago
- see https://github.com/thangbui/geepee for a faster implementation☆36Updated 7 years ago
- AAAI & CVPR 2016: Preconditioned Stochastic Gradient Langevin Dynamics (pSGLD)☆35Updated 6 years ago
- Additive Gaussian Process Bandits - version 1.0☆25Updated 7 years ago
- ☆39Updated 5 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
- Gaussian processes in TensorFlow with modifications to allow inter-domain inducing variables☆13Updated 7 years ago
- Convolutional Gaussian processes based on GPflow.☆95Updated 6 years ago
- We use a modified neural network instead of Gaussian process for Bayesian optimization.☆107Updated 7 years ago
- Implementations of the ICML 2017 paper (with Yarin Gal)☆39Updated 6 years ago
- Multiplicative Normalizing Flow (MNF) posteriors for variational Bayesian neural networks☆64Updated 4 years ago
- Repo for a paper about constructing priors on very deep models.☆69Updated 8 years ago
- Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019☆41Updated last year
- code for stochastic expectation propagation☆16Updated 8 years ago
- Python implementation of the PR-SSM.☆51Updated 6 years ago
- Implementation of Stochastic Gradient MCMC algorithms☆40Updated 7 years ago
- Code to related to my NIPS 2016 paper☆10Updated 7 years ago
- Implementation of stochastic variational inference for Bayesian hidden Markov models.☆65Updated 7 years ago
- Code for doubly stochastic gradients☆25Updated 9 years ago
- Reducing Reparameterization Gradient Variance code.☆33Updated 7 years ago
- ☆28Updated 5 years ago
- A tensorflow implementation of VAE training with Renyi divergence☆31Updated 8 years ago
- NeurIPS 2016. Linear-time interpretable nonparametric two-sample test.☆62Updated 6 years ago
- Exercises for the Tutorial on Approximate Bayesian Inference at the Data Science Summer School 2018☆22Updated 6 years ago
- An implementation of "Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles" (http://arxiv.org/abs/1612.01474)☆34Updated 7 years ago
- TensorFlow implementation of Bayes-by-Backprop algorithm from "Weight Uncertainty in Neural Networks" paper☆51Updated 5 years ago
- Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326☆71Updated 6 years ago
- NeurIPS 2017 best paper. An interpretable linear-time kernel goodness-of-fit test.☆66Updated 5 years ago
- Code for the paper Implicit Weight Uncertainty in Neural Networks☆65Updated 4 years ago