fab-torch
FAB algorithm
An implementation of the Flow Annealed Importance Sampling Bootstrap algorithm in Python.
Flow Annealed Importance Sampling Bootstrap (FAB). ICLR 2023.
51 stars
2 watching
6 forks
Language: Python
last commit: 8 months ago annealed-importance-samplingboltzmann-distributionboltzmann-generatornormalizing-flow
Related projects:
| Repository | Description | Stars |
|---|---|---|
| lollcat/fab-jax-old | An implementation of Flow Annealed Importance Sampling Bootstrap (FAB) in JAX for probabilistic machine learning | 0 |
 ljb121002/fednar | A Python implementation of federated optimization algorithm with normalized annealing regularization. | 6 |
 cyrilli/async-linucb | Implementation of algorithms for federated linear bandits in multi-agent environments | 1 |
 autodesk/xlb | A library that accelerates the solution of fluid dynamics problems using a massively parallel lattice Boltzmann method. | 229 |
 jorgecastilloprz/fabprogresscircle | Provides a material progress circle around a FloatingActionButton. | 1,247 |
 alikaraali/edge-based-dfe-tip2018 | An image processing project implementing edge-based defocus blur estimation with adaptive scale selection | 18 |
 aimclub/fedot | An automated machine learning framework that generates optimal machine learning pipelines for various real-world problems. | 644 |
 sebgao/lip | Implementations of Local Importance-based Pooling (LIP) in PyTorch for image classification tasks. | 220 |
 deepmed-lab-ecnu/deeprft-aaai2023 | A deep learning-based image deblurring system that explores the impact of frequency selection on restoration quality | 18 |
 asappresearch/flambe | An ML framework for accelerating research and its integration into production workflows | 262 |
 acerbilab/bads | An optimization algorithm designed to fit computational models in the absence of gradient information or noisy objective functions. | 246 |
 buaa-cst/ilrg | Recovery method for Federated Learning datasets using gradients to estimate instance-wise batch label restoration | 5 |
 pp00704831/banet-tip-2022 | A PyTorch implementation of an attention network for dynamic scene deblurring | 37 |
 ibm/fl-arbitrary-participation | Analyzes Federated Learning with Arbitrary Client Participation using various optimization strategies and datasets. | 4 |
 kkkls/fftformer | An image deblurring method using transformer architecture | 251 |