Search Results for author: Simon Frieder

Found 11 papers, 6 papers with code

Data for Mathematical Copilots: Better Ways of Presenting Proofs for Machine Learning

no code implementations19 Dec 2024 Simon Frieder, Jonas Bayer, Katherine M. Collins, Julius Berner, Jacob Loader, András Juhász, Fabian Ruehle, Sean Welleck, Gabriel Poesia, Ryan-Rhys Griffiths, Adrian Weller, Anirudh Goyal, Thomas Lukasiewicz, Timothy Gowers

The suite of datasets commonly used to train and evaluate the mathematical capabilities of AI-based mathematical copilots (primarily large language models) exhibit several shortcomings.

Math

Newclid: A User-Friendly Replacement for AlphaGeometry

1 code implementation18 Nov 2024 Vladmir Sicca, Tianxiang Xia, Mathïs Fédérico, Philip John Gorinski, Simon Frieder, Shangling Jui

Newclid contains a symbolic solver called DDARN (derived from DDAR-Newclid), which is a significant refactoring and upgrade of AlphaGeometry's DDAR symbolic solver by being more user-friendly - both for the end user as well as for a programmer wishing to extend the codebase.

Dimension-independent learning rates for high-dimensional classification problems

no code implementations26 Sep 2024 Andres Felipe Lerma-Pineda, Philipp Petersen, Simon Frieder, Thomas Lukasiewicz

Thereafter, we prove the existence of a neural network with bounded weights approximating a classification function.

Large Language Models for Mathematicians

no code implementations7 Dec 2023 Simon Frieder, Julius Berner, Philipp Petersen, Thomas Lukasiewicz

Large language models (LLMs) such as ChatGPT have received immense interest for their general-purpose language understanding and, in particular, their ability to generate high-quality text or computer code.

Language Models as a Service: Overview of a New Paradigm and its Challenges

no code implementations28 Sep 2023 Emanuele La Malfa, Aleksandar Petrov, Simon Frieder, Christoph Weinhuber, Ryan Burnell, Raza Nazar, Anthony G. Cohn, Nigel Shadbolt, Michael Wooldridge

This paper has two goals: on the one hand, we delineate how the aforementioned challenges act as impediments to the accessibility, replicability, reliability, and trustworthiness of LMaaS.

Benchmarking

Mathematical Capabilities of ChatGPT

2 code implementations NeurIPS 2023 Simon Frieder, Luca Pinchetti, Alexis Chevalier, Ryan-Rhys Griffiths, Tommaso Salvatori, Thomas Lukasiewicz, Philipp Christian Petersen, Julius Berner

We investigate the mathematical capabilities of two iterations of ChatGPT (released 9-January-2023 and 30-January-2023) and of GPT-4 by testing them on publicly available datasets, as well as hand-crafted ones, using a novel methodology.

Elementary Mathematics Math +2

Associative Memories via Predictive Coding

no code implementations NeurIPS 2021 Tommaso Salvatori, Yuhang Song, Yujian Hong, Simon Frieder, Lei Sha, Zhenghua Xu, Rafal Bogacz, Thomas Lukasiewicz

We conclude by discussing the possible impact of this work in the neuroscience community, by showing that our model provides a plausible framework to study learning and retrieval of memories in the brain, as it closely mimics the behavior of the hippocampus as a memory index and generative model.

Hippocampus Retrieval

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