arXiv AI
Oct 9

The Polytopal Neural Network

The paper introduces Polytopal Neural Networks (PNNs), a framework that enforces a polytope-based structure on neural network representations to extract distinct layer-wise aspects. By using learned corpus representations and an amortized simplex inference procedure, PNNs preserve meaningful latent structures with minimal performance loss and yield compressed representations comparable to vector‑quantized (VQ) methods. The authors demonstrate that PNNs provide a new, interpretable approach to VQ deep learning training while maintaining competitive performance.

By A. Emilie J. Wedenborg, Anders V. N{\o}rskov, Teresa Dorszewski, Kristoffer Wickstr{\o}m, Morten M{\o}rup
arXiv Machine Learning
Oct 9

Dissecting Representation Structure in Vision Transformers: A Rigorous Architectural Study

The paper presents a detailed study of feature representations in Vision Transformers (ViTs), revealing that feature collapse at initialization causes redundancy and proposing a reduction scheme to address it. It demonstrates that entropy and minimum eigenvalue metrics reliably predict generalization, and that token‑space features and linear submodule outputs are particularly informative. The authors develop a proxy that improves ranking of ViT architectures by 18‑48% over previous baselines, enabling the selection of models with higher accuracy at comparable or lower computational cost.

By Kim-Cuc Nguyen, Ngai-Man Cheung