siboehm / NormalizingFlowNetworkLinks
Bayesian and Maximum Likelihood Implementation of the Normalizing Flow Network (NFN): https://arxiv.org/abs/1907.08982
☆21Updated 4 years ago
Alternatives and similar repositories for NormalizingFlowNetwork
Users that are interested in NormalizingFlowNetwork are comparing it to the libraries listed below
Sorting:
- Repository for DTU Special Course, focusing on Variational Inference using Normalizing Flows (VINF). Supervised by Michael Riis Andersen☆25Updated 5 years ago
- Implementations of Normalizing Flows in Pytorch/Pyro☆19Updated 5 years ago
- Library for Bayesian Neural Networks in PyTorch (first version as published in ProbProg2020)☆42Updated 3 years ago
- We got a stew going!☆27Updated last year
- Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods☆20Updated 3 years ago
- Official code for the ICLR 2021 paper Neural ODE Processes☆74Updated 3 years ago
- Riemannian Convex Potential Maps☆67Updated 2 years ago
- Code for "'Hey, that's not an ODE:' Faster ODE Adjoints via Seminorms" (ICML 2021)☆87Updated 2 years ago
- Combining smooth constraint for building DAG with normalizing flow in order to replace autoregressive transformations while keeping tract…☆45Updated last year
- Bayesian algorithm execution (BAX)☆50Updated 3 years ago
- Code for: "Neural Rough Differential Equations for Long Time Series", (ICML 2021)☆118Updated 4 years ago
- Normalizing Flows with a resampled base distribution☆47Updated 2 years ago
- Implementations of normalizing flows using python and tensorflow☆24Updated 8 months ago
- Simple, extensible implementations of some meta-learning algorithms in Jax☆10Updated 4 years ago
- An ultra-lightweight JAX implementation of sparse Gaussian processes via pathwise sampling.☆22Updated 4 years ago
- Normalizing Flows using JAX☆84Updated last year
- ☆52Updated 2 years ago
- A variational method for fast, approximate inference for stochastic differential equations.☆44Updated 7 years ago
- Vector Quantile Regression☆19Updated 4 months ago
- Neural likelihood-free methods in PyTorch.☆39Updated 5 years ago
- Implementation of stochastic variational inference for differentially deep gaussian processes☆15Updated 6 years ago
- ☆54Updated last year
- A library for uncertainty quantification based on PyTorch☆121Updated 3 years ago
- Variational inference for hierarchical dynamical systems☆48Updated last year
- Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)☆20Updated 6 years ago
- ☆15Updated 2 years ago
- Implementation of the Convolutional Conditional Neural Process☆125Updated 4 years ago
- "How to Trust Your Diffusion Models: A Convex Optimization Approach to Conformal Risk Control"☆18Updated 2 months ago
- [NeurIPS 2020] Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters (AHGP)☆22Updated 4 years ago
- Code repo for "Function-Space Distributions over Kernels"☆32Updated 4 years ago