arXiv:2608. 11064v1 Announce Type: cross Abstract: Artificial intelligence (AI) has become a powerful approach to solving complex problems in critical domains.
By Ali Saleh, Abdul Karim Gizzini, Mohamad Ghassany, Ali J. Ghandour
arXiv:2608.29279v1 Announce Type: new
Abstract: Deep learning is often criticized for its theoretical research lagging behind practice. To make deep learning easier to understand, the entropy space t...
By Li Li, Tong Zhang, Wentao Yu, Zuobin Wang
The paper investigates how multilayer perceptrons (MLPs) learn features in regression tasks with clustered data. It finds that instead of forming a single global low‑dimensional representation, MLPs develop monosemantic specialized neurons—each neuron aligns strongly with a specific predictive feature relevant to a particular region of the input space. This specialization results in a collection of local low‑dimensional representations, giving MLPs a provable data‑efficiency advantage over methods that rely on a global representation.
By Amirhesam Abedsoltan, Enric Boix-Adsera, Fivos Kalogiannis, Mikhail Belkin
arXiv:2608.24007v1 Announce Type: new
Abstract: Understanding how neural networks learn and organize features is central to understanding their behavior. Much existing theory of feature learning has...
By Amirhesam Abedsoltan, Enric Boix-Adsera, Fivos Kalogiannis, Mikhail Belkin
The paper introduces magnitude potential, a metric derived from metric magnitude theory, to assess how well a point is represented by a set. By computing the ratio of magnitude potential relative to a class versus the entire dataset at the logit layer, the authors find correlations with Feldman memorization scores and detect structural changes in decision boundaries, including grokking in modular arithmetic. This ratio remains informative even when neural collapse is suppressed, highlighting its robustness in capturing intra‑ and inter‑class geometric structure.
By Sahel Torkamani, Henry Gouk, Rik Sarkar
Tabular-to-image methods have emerged as novel approaches to leverage the high predictive performance of convolutional neural networks and vision transformers. They convert tabular data into image representations, mapping each feature at a fixed pixel location derived from a dimensionality-reduction method (e.