Normalizing flows in PyTorch
-
Updated
Jun 6, 2024 - Python
Normalizing flows in PyTorch
Normalizing flows for neuro-symbolic AI
D<ee>p learning [dev library]
Normalizing-flow enhanced sampling package for probabilistic inference in Jax
nessai: Nested Sampling with Artificial Intelligence
PyTorch Lightning Implementation of Diffusion, GAN, VAE, Flow models
A flow-based generative ML model for calorimeter showers in particle detectors
Implementations of Infinitesimal Continuous Normalizing Flows Algorithms in Julia
pocoMC: A Python implementation of Preconditioned Monte Carlo for accelerated Bayesian Computation
A Julia framework for invertible neural networks
Nomalizing flows for orbita-free DFT
Applying amortizing neural posterior estimation for non-linear mixed effects models
This repository contains my solutions to the lab sessions for the course CS F437: Generative AI.
repo for practicing DL/genAI
Biology-driven deep generative model for cell-type annotation in cytometry. Scyan is an interpretable model that also corrects batch-effect and can be used for debarcoding or population discovery.
Maximum Entropy Reinforcement Learning via Energy-Based Normalizing Flow
WISER: multimodal variational inference for full-waveform inversion without dimensionality reduction
A set of notebooks related to convex optimization, variational inference and numerical methods for signal processing, machine learning, deep learning, graph analysis, bayesian programming, statistics or astronomy.
An extension of LightGBM to probabilistic modelling
Add a description, image, and links to the normalizing-flows topic page so that developers can more easily learn about it.
To associate your repository with the normalizing-flows topic, visit your repo's landing page and select "manage topics."