arXiv subjects

Abdurrahman Hadi Erturk

Publications and source records attributed to Abdurrahman Hadi Erturk.

4 recordsLinked to original sources

arXiv2026

Counting Connected and Disconnected Ways to Assemble a Jigsaw Puzzle

A jigsaw puzzle may be assembled in many different ways. Some assembly sequences remain connected throughout, while others temporarily build separate parts of the puzzle before joining them together. By representing the puzzle as a graph, these assembly sequences become vertex orderings that can be counted using recent results from graph theory. We apply this framework to enumerate three natural assembly strategies: connected assembly, assembly beginning from several disconnected pieces, and assembly in which new disconnected sections may be started during the process. The resulting counts reveal that, even for modest puzzle sizes, connectivity-preserving assembly sequences are greatly outnumbered by those that pass through disconnected intermediate stages.

math.GM↗

arXiv2026

Successive vertex orderings of graphs

A successive vertex ordering of a graph is a linear ordering of its vertices in which every vertex except the first has at least one neighbour appearing earlier. Such orderings arise naturally in incremental growth and connectivity-preserving constructions, where vertices are added sequentially and must attach to the existing structure. We derive an exact formula for the number of successive vertex orderings of any finite connected graph. The formula is obtained via an inclusion--exclusion argument over independent sets and depends on two explicit combinatorial parameters, one of which is defined recursively. The result applies to all finite connected graphs without requiring regularity or symmetry assumptions. We also express the enumeration as a weighted generating polynomial over independent sets; its value at $x = -1$ recovers the total count of successive orderings, and the $k$-th derivative at this point encodes the number of orderings in which exactly $k$ non-first vertices appear before all of their neighbours.

math.CO↗

arXiv2026

Decoupling Dynamical Richness from Representation Learning: Towards Practical Measurement

Dynamic feature transformation (the rich regime) does not always align with predictive performance (better representation), yet accuracy is often used as a proxy for richness, limiting analysis of their relationship. We propose a computationally efficient, performance-independent metric of richness grounded in the low-rank bias of rich dynamics, which recovers neural collapse as a special case. The metric is empirically more stable than existing alternatives and captures known lazy-torich transitions (e.g., grokking) without relying on accuracy. We further use it to examine how training factors (e.g., learning rate) relate to richness, confirming recognized assumptions and highlighting new observations (e.g., batch normalization promotes rich dynamics). An eigendecomposition-based visualization is also introduced to support interpretability, together providing a diagnostic tool for studying the relationship between training factors, dynamics, and representations.

stat.ML↗

arXiv2025

Feature learning is decoupled from generalization in high capacity neural networks

Neural networks outperform kernel methods, sometimes by orders of magnitude, e.g. on staircase functions. This advantage stems from the ability of neural networks to learn features, adapting their hidden representations to better capture the data. We introduce a concept we call feature quality to measure this performance improvement. We examine existing theories of feature learning and demonstrate empirically that they primarily assess the strength of feature learning, rather than the quality of the learned features themselves. Consequently, current theories of feature learning do not provide a sufficient foundation for developing theories of neural network generalization.

cs.LG↗