alshedivat / DeterminantalPointProcesses.jlLinks
Determinantal Point Processes in Julia
☆12Updated 5 years ago
Alternatives and similar repositories for DeterminantalPointProcesses.jl
Users that are interested in DeterminantalPointProcesses.jl are comparing it to the libraries listed below
Sorting:
- Example implementation of the Bayesian neural network in "Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteri…☆30Updated 5 years ago
- NeurIPS 2016. Linear-time interpretable nonparametric two-sample test.☆64Updated 7 years ago
- This is code associated with the paper: Broderick, T, Boyd, N, Wibisono, A, Wilson, AC, and Jordan, MI. Streaming variational Bayes. Neur…☆41Updated 11 years ago
- NeurIPS 2017 best paper. An interpretable linear-time kernel goodness-of-fit test.☆67Updated 6 years ago
- Implementation of Stochastic Gradient MCMC algorithms☆41Updated 8 years ago
- Gaussian Processes in Pytorch☆75Updated 5 years ago
- Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326☆71Updated 7 years ago
- Code for "Deep Convolutional Networks as shallow Gaussian Processes"☆39Updated 6 years ago
- Hyperparameter optimization with approximate gradient☆66Updated 4 years ago
- Deep exponential families (DEFs)☆55Updated 7 years ago
- ☆40Updated 6 years ago
- Multiplicative Normalizing Flow (MNF) posteriors for variational Bayesian neural networks☆66Updated 5 years ago
- Convolutional Gaussian processes based on GPflow.☆95Updated 7 years ago
- Reducing Reparameterization Gradient Variance code.☆33Updated 8 years ago
- MADE: Masked Autoencoder for Distribution Estimation☆103Updated 5 years ago
- Code for the paper Implicit Weight Uncertainty in Neural Networks☆65Updated 5 years ago
- Repo for a paper about constructing priors on very deep models.☆73Updated 9 years ago
- Replication of the paper "Variational Dropout and the Local Reparameterization Trick" using Lasagne.☆33Updated 7 years ago
- Probabilistic classification in PyTorch/TensorFlow/scikit-learn with Fenchel-Young losses☆192Updated 2 years ago
- Summaries and minimal implementations of ML / statistics research articles.☆39Updated 4 years ago
- An implementation of "Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles" (http://arxiv.org/abs/1612.01474)☆34Updated 8 years ago
- code for stochastic expectation propagation☆16Updated 9 years ago
- Open access book on variational Bayesian methods written collaboratively☆28Updated 10 years ago
- A generic Monte Carlo method based on the Gumbel-Max trick.☆32Updated 9 years ago
- Stochastic Gradient Riemannian Langevin Dynamics☆34Updated 10 years ago
- Variational Fourier Features☆85Updated 4 years ago
- Proximal Backpropagation - a neural network training algorithm that takes implicit instead of explicit gradient steps☆42Updated 6 years ago
- Variational Message Passing for Structured VAE (Code for ICLR 2018 paper)☆45Updated 7 years ago
- We use a modified neural network instead of Gaussian process for Bayesian optimization.☆108Updated 8 years ago
- Deep Generative Models with Stick-Breaking Priors☆96Updated 9 years ago