mj-will / augmented-flows
Pytorch implementation of the basic idea presented in https://arxiv.org/abs/2002.07101
☆11Updated 4 years ago
Related projects: ⓘ
- [AISTATS2020] The official repository of "Invertible Generative Modling using Linear Rational Splines (LRS)".☆20Updated last year
- PyTorch implementation of the OT-Flow approach in arXiv:2006.00104☆49Updated last month
- ☆28Updated 2 years ago
- Code for the paper Semi-Conditional Normalizing Flows for Semi-Supervised Learning☆28Updated 3 years ago
- PyTorch implementation of Continuously Indexed Flows paper, with many baseline normalising flows☆29Updated 3 years ago
- Code for the research paper "HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference".☆19Updated 3 years ago
- Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"☆33Updated last year
- Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'☆26Updated 3 years ago
- Neural likelihood-free methods in PyTorch.☆38Updated 4 years ago
- Glow and RealNVP with tensorflow 2.0 and tensorflow probability☆31Updated last year
- ☆21Updated 4 years ago
- ☆15Updated 2 years ago
- Pytorch version of "Deep Convolutional Networks as shallow Gaussian Processes" by Adrià Garriga-Alonso, Carl Rasmussen and Laurence Aitch…☆32Updated 4 years ago
- Featurized Density Ratio Estimation☆19Updated 3 years ago
- The PyTorch implementation of the GLF☆21Updated 2 years ago
- code submission to NeurIPS2019☆13Updated last year
- ☆31Updated 2 years ago
- Source code for Large-Scale Wasserstein Gradient Flows (NeurIPS 2021)☆29Updated 2 years ago
- ☆62Updated 7 months ago
- ☆52Updated last month
- Code for Understanding and Mitigating Exploding Inverses in Invertible Neural Networks (AISTATS 2021) http://arxiv.org/abs/2006.09347☆29Updated 4 years ago
- ☆67Updated last year
- Multivariate Gaussian distributions for Tensorflow.☆22Updated 4 years ago
- Official PyTorch BIVA implementation (BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling)☆81Updated last year
- Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)☆72Updated 11 months ago
- Pytorch implementation of DGflow (ICLR 2021).☆16Updated 3 years ago
- PyTorch implementation of Bidirectional Monte Carlo, Annealed Importance Sampling, and Hamiltonian Monte Carlo.☆52Updated 3 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 4 years ago
- Riemannian Convex Potential Maps☆68Updated last year
- beta-NLL introduced in our paper "On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks" ICLR 2022☆33Updated 2 years ago