A Toolkit for Distributional Control of Generative Models
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Updated
Sep 4, 2023 - Python
A Toolkit for Distributional Control of Generative Models
[AAAI20] TensorFlow implementation of the Collaborative Sampling in Generative Adversarial Networks
pMoSS (p-value Model using the Sample Size) is a Python code to model the p-value as an n-dependent function using Monte Carlo cross-validation. Exploits the dependence on the sample size to characterize the differences among groups of large datasets
Codebase for "Greedy Shapley Client Selection for Communication-Efficient Federated Learning"
Accompanying source code to my Bachelor's thesis at TUHH
Bayesian Non-Parametric Image Segmentation using HDP-MRF
A basic PyTorch implementation of the Collaborative Sampling in Generative Adversarial Networks
This algorithm calculates the zero-point energy of a molecular system by monte-carlo sampling the system's potential energy surface.
This program computes the particle pair HBT correlation from Monte-Carlo samples of emitted particles
My works for EE 511 - Simulation Methods For Stochastic Systems - Spring 2018 - Graduate Coursework at USC - Dr. Osonde A. Osoba
Research Paper about adversarial search
Descriptor and Generator components of CoopNet
Sampling and resampling techniques for random sample generation, estimation, and simulation
Variational autoencoders using Kera's modular design
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