1 citations · 3 across the 10 of their papers we have counts for
10 papers
Prototype Transformer: Towards Language Model Architectures Interpretable by Design
Yordan Yordanov, Matteo Forasassi, Bayar Menzat +6
While state-of-the-art language models (LMs) surpass most humans in certain domains, their reasoning remains largely opaque, reducing trust and increasing the risk of deception and…
Faster Predictive Coding Networks via Better Initialization
Luca Pinchetti, Simon Frieder, Thomas Lukasiewicz +1
Research aimed at scaling up neuroscience inspired learning algorithms for neural networks is accelerating. Recently, a key research area has been the study of energy-based learnin…
Towards the Training of Deeper Predictive Coding Neural Networks
Chang Qi, Matteo Forasassi, Thomas Lukasiewicz +1
Predictive coding networks are neural models that perform inference through an iterative energy minimization process, whose operations are local in space and time. While effective…
Shh, don't say that! Domain Certification in LLMs
Cornelius Emde, Alasdair Paren, Preetham Arvind +6
Large language models (LLMs) are often deployed to perform constrained tasks, with narrow domains. For example, customer support bots can be built on top of LLMs, relying on their…
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…
Fool Me Once? Contrasting Textual and Visual Explanations in a Clinical Decision-Support Setting
Maxime Kayser, Bayar Menzat, Cornelius Emde +7
The growing capabilities of AI models are leading to their wider use, including in safety-critical domains. Explainable AI (XAI) aims to make these models safer to use by making th…
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…
Towards Certification of Uncertainty Calibration under Adversarial Attacks
Cornelius Emde, Francesco Pinto, Thomas Lukasiewicz +2
Since neural classifiers are known to be sensitive to adversarial perturbations that alter their accuracy, \textit{certification methods} have been developed to provide provable gu…
Associative Memories in the Feature Space
Tommaso Salvatori, Beren Millidge, Yuhang Song +2
An autoassociative memory model is a function that, given a set of data points, takes as input an arbitrary vector and outputs the most similar data point from the memorized set. H…