AWehenkel / UMNNLinks
Implementation of Unconstrained Monotonic Neural Network and the related experiments. These architectures are particularly useful for modelling monotonic transformations in normalizing flows.
☆123Updated 8 months ago
Alternatives and similar repositories for UMNN
Users that are interested in UMNN are comparing it to the libraries listed below
Sorting:
- Masked Autoregressive Flow☆215Updated last year
- code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm"☆100Updated 6 years ago
- Pytorch implementation of Neural Processes for functions and images☆232Updated 3 years ago
- Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"☆171Updated 3 years ago
- Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.☆224Updated last year
- Code for the paper "Bayesian Neural Network Priors Revisited"☆57Updated 4 years ago
- Implementation of the Convolutional Conditional Neural Process☆125Updated 4 years ago
- A community repository for benchmarking Bayesian methods☆110Updated 3 years ago
- Manifold-learning flows (ℳ-flows)☆230Updated 4 years ago
- Neural Spline Flow, RealNVP, Autoregressive Flow, 1x1Conv in PyTorch.☆282Updated last year
- Code repo for "Function-Space Distributions over Kernels"☆32Updated 4 years ago
- Deep neural network kernel for Gaussian process☆208Updated 4 years ago
- Code for random Fourier features based on Rahimi and Recht's 2007 paper.☆56Updated 4 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆85Updated 5 years ago
- PyTorch implementation of the OT-Flow approach in arXiv:2006.00104☆56Updated last year
- Normalizing Flows with a resampled base distribution☆47Updated 2 years ago
- Implementation of random Fourier features for kernel method, like support vector machine and Gaussian process model☆101Updated 9 months ago
- ☆151Updated 2 years ago
- Regularized Neural ODEs (RNODE)☆84Updated 4 years ago
- Code Repo for "Subspace Inference for Bayesian Deep Learning"☆83Updated last year
- Official code for the ICLR 2021 paper Neural ODE Processes☆74Updated 3 years ago
- 🤿 Implementation of doubly stochastic deep Gaussian Process using GPflow and TensorFlow 2.0☆26Updated last year
- PyTorch code of "Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows" (NeurIPS 2020)☆48Updated 4 years ago
- AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'☆89Updated 4 years ago
- Code for Randomly Projected Additive Gaussian Processes☆25Updated 5 years ago
- Code for efficiently sampling functions from GP(flow) posteriors☆72Updated 4 years ago
- Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)☆20Updated 6 years ago
- Sampling with gradient-based Markov Chain Monte Carlo approaches☆107Updated last year
- Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021☆24Updated 2 years ago