Synthetic Data - Machine Learning Techniques

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A Synthetic Stock Exchange Played with Real Money

Not only that, but you can predict — more precisely compute with absolute certainty — what the value of any stock will be tomorrow. Transaction fees are well below 0.05% and the market, at least in the version presented here, is fair: in other words, a zero-sum game if you play by luck. If instead […]

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Autonomous Driving: Boosting Optical Flow with Synthetic Data

Optical flow is defined as the task of estimating per-pixel motion between video frames. Optical flow models take two sequential frames as input and return as output a flow vector that predicts where each pixel in the first frame will be in the second frame. Optical flow is an important task for autonomous driving, but […]

Featured Posts Machine Learning Stochastic Systems Synthetic Data Visualization

Generating and Videolizing Agglomerative Processes

This short article explains how to efficiently simulate the evolution of agglomerative processes, and visualize their behavior with data animations. I use a generic, simple model for illustration purposes: atoms, initially consisting of one electron, collide and merge over time, with a pre-specified maximum number of electrons per atom: the maximum limit. Given enough time, […]

Featured Posts Machine Learning Statistical Science Synthetic Data

Smart Grid Search for Faster Hyperparameter Tuning

The objective is two-fold. First, I introduce a 2-parameter generalization of the discrete geometric and zeta distributions. Indeed, a combination of both. It allows you to simultaneously match the variance and mean in observed data, thanks to the two parameters p and α. To the contrary, each distribution taken separately only has one parameter, and […]

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Data Synthetization: enhanced GANs vs Copulas

Using case studies, I compare generative adversarial networks (GANs) with copulas to synthesize tabular data. I discuss back-end and front-end improvements to help GANs better replicate the correlation structure present in the real data. Likewise, I discuss methods to further improve copulas, including transforms, the use of separate copulas for each population segment, and parametric […]

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Data Synthetization Explained in One Picture

The diagram is organized as follows. Dashed blue lines are associated to GANs (generative adversarial networks), where the goal is to produce a sequence of synthetic datasets that get better and better at mimicking the structure present in the real data, over successive iterations. The diagram features 5 such iterations, with the synthetized datasets denoted […]

Books Data Sets Explainable AI Featured Posts Machine Learning Synthetic Data Visualization

New Book on Synthetic Data: Version 3.0 Just Released

Update on March 3, 2023: Version 4.0 has been released and now replaces version 3.0 on the e-Store. It contains a new full chapter on enhanced generative adversarial networks (GANs) with comparison to copula-based methods for data synthetization, with illustrations on real-life datasets. The book has considerably grown since version 1.0. It started with synthetic […]

Data Sets Featured Posts Machine Learning Synthetic Data Time Series Visualization

New Interpolation Methods for Data Synthetization and Prediction

Entitled “New Interpolation Methods for Synthetization and Prediction”, the full version in PDF format is accessible in the “Free Books and Articles” section, here. This article is an extract from my book “Synthetic Data and Generative AI”, available here. I describe little-known original interpolation methods with applications to real-life datasets. These simple techniques are easy to implement and can […]

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Synthetizing the Insurance Dataset Using Copulas: Towards Better Synthetization

This article is an extract from my book “Synthetic Data and Generative AI”, available here. In the context of synthetic data generation, I’ve been asked a few times to provide a case study focusing on real-life tabular data used in the finance or health industry. Here we go: this article fills this gap. The purpose is […]

Data Sets Featured Posts Machine Learning Statistical Science Synthetic Data

Military-grade Fast Random Number Generator Based on Quadratic Irrationals

This article is an extract from my book “Synthetic Data and Generative AI”, available here. There are very few serious articles in the literature dealing with digits of irrational numbers to build a pseudo-random number generator (PRNG). It seems that this idea was abandoned long ago due to the computational complexity and the misconception that such […]

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