Framework for analysis of Normalizing Flows based Generative models. Analyses include: similarity between classes, dimensionality reduction (PCA, UMAP), experimental image compression.
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Updated
Aug 28, 2022 - Python
Framework for analysis of Normalizing Flows based Generative models. Analyses include: similarity between classes, dimensionality reduction (PCA, UMAP), experimental image compression.
Using ML to Simulate Distributions of Observables at the LHC
An implementation of the TME from the Reinforcement Learning course given at Sorbonne University.
Demo PyTorch code for "Variational Inference with Normalizing Flows" (ICML 2015)
An Invertible Neural Network using Variational-Inference to estimate the model uncertainty
practice generative AI with MNIST
Unofficial implementation of Hierarchical Flow (image-to-image) for didactic purposes
A short tutorial on normalizing flows using jupyter slides
Toy implementations of some common generative models
Statistical framework to perform parameter estimation with normalizing flows
Final project and home assignments from Generative Models in Machine Learning course
Surjection layers for density estimation with normalizing flows
Code for "Diffeomorphic Measure Matching with Kernels for Generative Modeling.'
repo for practicing DL/genAI
Code for paper "Gradient-assisted calibration for financial agent-based models"
An implementation of variational normalizing flows using TF2
causal discovery using likelihood (normalizing flow)
Implementation of generative models for the design of small molecules
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