AWehenkel / UMNNLinks
Implementation of Unconstrained Monotonic Neural Network and the related experiments. These architectures are particularly useful for modelling monotonic transformations in normalizing flows.
☆125Updated last month
Alternatives and similar repositories for UMNN
Users that are interested in UMNN are comparing it to the libraries listed below
Sorting:
- code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm"☆101Updated 6 years ago
- Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.☆227Updated last year
- Masked Autoregressive Flow☆218Updated last year
- Pytorch implementation of Neural Processes for functions and images☆236Updated 3 years ago
- Regularized Neural ODEs (RNODE)☆81Updated 4 years ago
- A community repository for benchmarking Bayesian methods☆112Updated 4 years ago
- Implementation of random Fourier features for kernel method, like support vector machine and Gaussian process model☆103Updated last year
- Implementation of the Convolutional Conditional Neural Process☆128Updated 4 years ago
- Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"☆171Updated 3 years ago
- Code for Randomly Projected Additive Gaussian Processes☆25Updated 6 years ago
- AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'☆90Updated 5 years ago
- Code repo for "Function-Space Distributions over Kernels"☆32Updated 4 years ago
- Code for the paper "Bayesian Neural Network Priors Revisited"☆58Updated 4 years ago
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆86Updated 5 years ago
- Bayesian Deep Learning with Stochastic Gradient MCMC Methods☆38Updated 4 years ago
- Official code for the ICLR 2021 paper Neural ODE Processes☆75Updated 3 years ago
- Sampling with gradient-based Markov Chain Monte Carlo approaches☆108Updated last year
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 6 years ago
- Code for random Fourier features based on Rahimi and Recht's 2007 paper.☆58Updated 5 years ago
- Neural Spline Flow, RealNVP, Autoregressive Flow, 1x1Conv in PyTorch.☆281Updated 2 years ago
- Deep neural network kernel for Gaussian process☆212Updated 5 years ago
- Reimplementation of Variational Inference with Normalizing Flows (https://arxiv.org/abs/1505.05770)☆241Updated 7 years ago
- PyTorch code of "Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows" (NeurIPS 2020)☆47Updated 5 years ago
- Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)☆20Updated 6 years ago
- ODE2VAE: Deep generative second order ODEs with Bayesian neural networks☆131Updated last year
- PyTorch implementation of the OT-Flow approach in arXiv:2006.00104☆57Updated last year
- Code Repo for "Subspace Inference for Bayesian Deep Learning"☆83Updated last year
- Manifold-learning flows (ℳ-flows)☆233Updated 5 years ago
- Random Fourier Features☆50Updated 8 years ago
- This repository contains code released by DiffEqML Research☆92Updated 3 years ago