arXiv:2603.23792v1 Announce Type: new
Abstract: Diffusion models often generate novel samples even when the learned score is only \emph{coarse} — a phenomenon not accounted for by the standard view of diffusion training as density estimation. In this…
arXiv:2603.23547v1 Announce Type: cross
Abstract: Independent component analysis is a core framework within blind source separation for recovering latent source signals from observed mixtures under statistical independence assumptions. In this work, w…
arXiv:2603.24333v1 Announce Type: cross
Abstract: Recently, Forr\’e (arXiv:2104.11547, 2021) introduced transitional conditional independence, a notion of conditional independence that provides a unified framework for both random and non-stochastic va…
arXiv:2603.23805v1 Announce Type: new
Abstract: Neural Collapse is a phenomenon that helps identify sparse and low rank structures in deep classifiers. Recent work has extended the definition of neural collapse to regression problems, albeit only meas…
arXiv:2603.23581v1 Announce Type: cross
Abstract: In clustering, strong dominance in the size of a particular cluster is often undesirable, motivating a measure of cluster size uniformity that can be used to filter such partitions. A basic requirement…
arXiv:2510.00479v2 Announce Type: replace-cross
Abstract: We numerically investigate the feasibility and limits of jointly estimating flow fields and unknown particle properties (e.g., position, size, and density) from Lagrangian particle tracking (LP…
arXiv:2603.22339v2 Announce Type: replace
Abstract: Chinchilla Approach 2 is among the most widely used methods for fitting neural scaling laws. Its parabolic approximation introduces systematic biases in compute-optimal allocation estimates, even on …
arXiv:2603.16661v2 Announce Type: replace
Abstract: Standard masked discrete diffusion models face limitations in reasoning tasks due to their inability to correct their own mistakes on the masking path. Since they rely on a fixed number of denoising …
arXiv:2603.24495v1 Announce Type: cross
Abstract: While the mathematical foundations of score-based generative models are increasingly well understood for unconstrained Euclidean spaces, many practical applications involve data restricted to bounded d…
arXiv:2405.17573v3 Announce Type: replace-cross
Abstract: We study Leaky ResNets, which interpolate between ResNets and Fully-Connected nets depending on an ‘effective depth’ hyper-parameter $\tilde{L}$. In the infinite depth limit, we study ‘represen…