VincentStimper / resampled-base-flowsLinks
Normalizing Flows with a resampled base distribution
☆47Updated 3 years ago
Alternatives and similar repositories for resampled-base-flows
Users that are interested in resampled-base-flows are comparing it to the libraries listed below
Sorting:
- Stochastic Normalizing Flows☆78Updated 3 years ago
- ☆51Updated 2 years ago
- Source code for Large-Scale Wasserstein Gradient Flows (NeurIPS 2021)☆36Updated 3 years ago
- Sampling with gradient-based Markov Chain Monte Carlo approaches☆108Updated last year
- Neural likelihood-free methods in PyTorch.☆39Updated 5 years ago
- Normalizing Flows using JAX☆84Updated last year
- Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling☆36Updated 3 years ago
- Implementation of Action Matching for the Schrödinger equation☆23Updated 2 years ago
- Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021☆24Updated 2 years ago
- Library for normalizing flows and neural flows.☆25Updated 3 years ago
- Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)☆20Updated 6 years ago
- [ICML 2024] Official implementation for "Beyond ELBOs: A Large-Scale Evaluation of Variational Methods for Sampling".☆36Updated 9 months ago
- Implementations of Normalizing Flows in Pytorch/Pyro☆19Updated 5 years ago
- Regularized Neural ODEs (RNODE)☆85Updated 4 years ago
- code for "Neural Conservation Laws A Divergence-Free Perspective".☆40Updated 2 years ago
- Implementation of Action Matching☆44Updated 2 years ago
- Official repository for "Categorical Normalizing Flows via Continuous Transformations"☆57Updated 4 years ago
- Sliced Iterative Generator (SIG) & Gaussianizing Iterative Slicing (GIS)☆37Updated 2 years ago
- Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods☆20Updated 3 years ago
- Implementation of normalizing flows in TensorFlow 2 including a small tutorial.☆147Updated last month
- PyTorch implementation of the OT-Flow approach in arXiv:2006.00104☆55Updated last year
- Flow Annealed Importance Sampling Bootstrap (FAB). ICLR 2023.☆65Updated last year
- Combining smooth constraint for building DAG with normalizing flow in order to replace autoregressive transformations while keeping tract…☆46Updated last year
- Laplace Redux -- Effortless Bayesian Deep Learning☆42Updated 3 months ago
- A set of tests for evaluating large-scale algorithms for Wasserstein-2 transport maps computation (NeurIPS 2021)☆43Updated 3 years ago
- Official code for the ICLR 2021 paper Neural ODE Processes☆74Updated 3 years ago
- Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"☆171Updated 3 years ago
- Codes for ICLR 21 paper: Neural Approximate Sufficient Statistics for Implicit Models☆20Updated 3 years ago
- Manifold-learning flows (ℳ-flows)☆230Updated 4 years ago
- Collecting research materials on neural samplers with diffusion/flow models☆58Updated 2 months ago