Thomas Brownback

Training models at cipher identification for cryptanalysis (link)

To study the impact of hyperparameters, I expanded the system to automatically train suites of LSTM models in bulk, hundreds or thousands at a time, each with a slightly different configuration. That created a new problem: how to compare all these models to understand which settings had the most impact on performance. So I built a bespoke visualization tool to quickly see and understand the exact impact of changes to the training data and the hyperparameters. Cipher identification is a great problem for learning about model performance and tuning because labeled training data is trivial to generate. Early findings indicate LSTMs are often robust at distinguishing many classical ciphers with minimal training, though they can struggle when learning to distinguish multiple categories all in one model.

Decompositional analysis of random midjourney prompts (link)

The Internet and other unreliable oracles: Gettier problems in the disinformation age (link)

Military-age male manpower model: Russia vs Ukraine (link)