arXiv:2602.18047v1 Announce Type: cross Abstract: City-scale person re-identification across distributed cameras must handle severe appearance changes from viewpoint, occlusion, and domain shift while complying with data protection rules that prevent sharing raw imagery. We introduce CityGuard, a topology-aware transformer for…
arXiv:2602.17688v1 Announce Type: new Abstract: Diffusion language models offer a compelling alternative to autoregressive code generation, enabling global planning and iterative refinement of complex program logic. However, existing approaches fail to respect the rigid structure of programming languages and, as…
arXiv:2602.18313v1 Announce Type: cross Abstract: Machine learning (ML) approaches have shown promising results for predicting molecular properties relevant for chemical process design. However, they are often limited by scarce experimental property data and lack thermodynamic consistency. As such, thermodynamics-informed ML,…
arXiv:2602.17689v1 Announce Type: new Abstract: Medical vision-language models show strong potential for joint reasoning over medical images and clinical text, but their performance often degrades under domain shift caused by variations in imaging devices, acquisition protocols, and reporting styles. Existing…
arXiv:2503.19605v4 Announce Type: replace Abstract: Understanding and certifying the generalization performance of machine learning algorithms — i.e. obtaining theoretical estimates of the test error from a finite training sample — is a central theme of statistical learning theory. Among the…
arXiv:2508.04581v2 Announce Type: replace-cross Abstract: Large language models have revolutionized AI applications, yet their high computational and memory demands hinder their widespread deployment. Existing compression techniques focus on intra-block optimizations (e.g., low-rank approximation or attention pruning), while the repetitive layered…
arXiv:2510.08570v2 Announce Type: replace Abstract: Neural networks are famously nonlinear. However, linearity is defined relative to a pair of vector spaces, $f:X to Y$. Leveraging the algebraic concept of transport of structure, we propose a method to explicitly identify non-standard…
arXiv:2601.11924v2 Announce Type: replace Abstract: We study cooperative stochastic multi-armed bandits with vector-valued rewards under adversarial corruption and limited verification. In each of $T$ rounds, each of $N$ agents selects an arm, the environment generates a clean reward vector, and…
arXiv:1803.09319v2 Announce Type: replace Abstract: Deep neural networks are often used to implement powerful generative models for real-world data. Notable applications include image denoising, as well as other classical inverse problems like compressed sensing and super-resolution. To provide a rigorous…
arXiv:2506.05647v4 Announce Type: replace Abstract: We study gradient-based data attribution, aiming to identify which training examples most influence a given output. Existing methods for this task either treat network parameters uniformly or rely on implicit weighting derived from Hessian approximations,…
