3 citations · 3 across the 5 of their papers we have counts for
5 papers
From superposition to sparse codes: interpretable representations in neural networks
David Klindt, Charles O'Neill, Patrik Reizinger +2
Understanding how information is represented in neural networks is a fundamental challenge in both neuroscience and artificial intelligence. Despite their nonlinear architectures,…
Self-Attention as a Parametric Endofunctor: A Categorical Framework for Transformer Architectures
Charles O'Neill
Self-attention mechanisms have revolutionised deep learning architectures, yet their core mathematical structures remain incompletely understood. In this work, we develop a categor…
Compute Optimal Inference and Provable Amortisation Gap in Sparse Autoencoders
Charles O'Neill, Alim Gumran, David Klindt
A recent line of work has shown promise in using sparse autoencoders (SAEs) to uncover interpretable features in neural network representations. However, the simple linear-nonlinea…
Disentangling Dense Embeddings with Sparse Autoencoders
Charles O'Neill, Christine Ye, Kartheik Iyer +1
Sparse autoencoders (SAEs) have shown promise in extracting interpretable features from complex neural networks. We present one of the first applications of SAEs to dense text embe…
Rice paddy disease classifications using CNNs
Charles O'Neill
Rice is a staple food in the world's diet, and yet huge percentages of crop yields are lost each year to disease. To combat this problem, people have been searching for ways to aut…