otokonoko8 / deep-Bayesian-nonparametrics-papers
The collection of papers about combining deep learning and Bayesian nonparametrics
☆118Updated 5 years ago
Related projects ⓘ
Alternatives and complementary repositories for deep-Bayesian-nonparametrics-papers
- The collection of recent papers about variational inference☆84Updated 5 years ago
- Sample code for running deterministic variational inference to train Bayesian neural networks☆100Updated 6 years ago
- A community repository for benchmarking Bayesian methods☆109Updated 2 years ago
- Understanding normalizing flows☆131Updated 4 years ago
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆80Updated 4 years ago
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆63Updated 5 years ago
- Code Repo for "Subspace Inference for Bayesian Deep Learning"☆80Updated 4 months ago
- ☆177Updated 5 years ago
- Code for the paper Gaussian process behaviour in wide deep networks☆48Updated 6 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 4 years ago
- ☆28Updated 5 years ago
- Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019☆41Updated last year
- PyTorch implementation of Neural Processes☆88Updated 5 years ago
- Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326☆71Updated 6 years ago
- Deep convolutional gaussian processes.☆78Updated 5 years ago
- A variational inference method with accurate uncertainty estimation. It uses a new semi-implicit variational family built on neural netwo…☆53Updated 3 weeks ago
- Gaussian Processes in Pytorch☆74Updated 4 years ago
- Deep Gaussian Processes with Doubly Stochastic Variational Inference☆147Updated 5 years ago
- MLSS2019 Tutorial on Bayesian Deep Learning☆90Updated 4 years ago
- PyTorch implementation of Bidirectional Monte Carlo, Annealed Importance Sampling, and Hamiltonian Monte Carlo.☆52Updated 3 years ago
- Pytorch implementation of Neural Processes for functions and images☆224Updated 2 years ago
- Code for "A Meta Transfer Objective For Learning To Disentangle Causal Mechanisms"☆125Updated 5 years ago
- Multiplicative Normalizing Flow (MNF) posteriors for variational Bayesian neural networks☆64Updated 4 years ago
- Pytorch implementation of Block Neural Autoregressive Flow☆175Updated 3 years ago
- Masked Autoregressive Flow☆203Updated 3 months ago
- Papers for Bayesian-NN☆314Updated 5 years ago
- Code for Randomly Projected Additive Gaussian Processes☆25Updated 4 years ago
- Reimplementation of Variational Inference with Normalizing Flows (https://arxiv.org/abs/1505.05770)☆227Updated 6 years ago
- PyTorch implementation of Stein Variational Gradient Descent☆41Updated last year