5 papers
Pattern Formation in Transformers
Erkan Turan, Gaspard Abel, Maks Ovsjanikov
What are the inductive biases of a Transformer architecture? Existing theory on how the forward pass shapes representations either considers whether Transformers escape from rank c…
Generative Drifting is Secretly Score Matching: a Spectral and Variational Perspective
Erkan Turan, Nicolas Dufour, Maks Ovsjanikov
Generative Modeling via Drifting~\citep{deng2026drifting} has recently achieved state-of-the-art one-step image generation through a kernel-based drift operator, yet its success is…
Beyond ReLU: Bifurcation, Oversmoothing, and Topological Priors
Erkan Turan, Gaspard Abel, Maysam Behmanesh +2
Graph Neural Networks (GNNs) learn node representations through iterative network-based message-passing. While powerful, deep GNNs suffer from oversmoothing, where node features co…
Graph Alignment via Dual-Pass Spectral Encoding and Latent Space Communication
Maysam Behmanesh, Erkan Turan, Maks Ovsjanikov
Graph alignment, the problem of identifying corresponding nodes across multiple graphs, is fundamental to numerous applications. Most existing unsupervised methods embed node featu…
Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization
Erkan Turan, Aristotelis Siozopoulos, Louis Martinez +3
Continuous Normalizing Flows (CNFs) enable elegant generative modeling but remain bottlenecked by their iterative nature requiring costly sampling and lacking interpretability of t…