WISE: full-Waveform variational Inference via Subsurface Extensions
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Updated
Jun 12, 2024 - Julia
WISE: full-Waveform variational Inference via Subsurface Extensions
pocoMC: A Python implementation of Preconditioned Monte Carlo for accelerated Bayesian Computation
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
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.
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