302 citations · 349 across the 11 of their papers we have counts for
3 papers · 2 filters
Data for Mathematical Copilots: Better Ways of Presenting Proofs for Machine Learning
Simon Frieder, Jonas Bayer, Sam Looi +13
The datasets and benchmarks commonly used to train and evaluate the mathematical capabilities of AI-based mathematical copilots (primarily large language models) exhibit several sh…
Dimension-independent learning rates for high-dimensional classification problems
Andres Felipe Lerma-Pineda, Philipp Petersen, Simon Frieder +1
We study the problem of approximating and estimating classification functions that have their decision boundary in the space. Functions of type arise naturally as s…
Benchmarking Predictive Coding Networks -- Made Simple
Luca Pinchetti, Chang Qi, Oleh Lokshyn +9
In this work, we tackle the problems of efficiency and scalability for predictive coding networks (PCNs) in machine learning. To do so, we propose a library, called PCX, that focus…