arXiv:2510.01190v1 Announce Type: cross Abstract: This work focuses on visualizing uncertainty of local divergence of two-dimensional vector fields. Divergence is one of the fundamental attributes of fluid flows, as it can help domain scientists analyze potential positions of sources (positive…
arXiv:2505.06595v2 Announce Type: replace Abstract: In this paper, we propose a method for transferring feature representation to lightweight student models from larger teacher models. We mathematically define a new notion called textit{perception coherence}. Based on this notion, we propose a…
arXiv:2510.01231v1 Announce Type: cross Abstract: This study addresses the reliability of automatic summarization in high-risk scenarios and proposes a large language model framework that integrates uncertainty quantification and risk-aware mechanisms. Starting from the demands of information overload and high-risk decision-making,…
arXiv:2110.03118v2 Announce Type: replace-cross Abstract: Kernel two-sample tests have been widely used, and the development of efficient methods for high-dimensional, large-scale data is receiving increasing attention in the big data era. However, existing methods, such as the maximum mean discrepancy…
arXiv:2510.01251v1 Announce Type: cross Abstract: Linking textual values in tabular data to their corresponding entities in a Knowledge Base is a core task across a variety of data integration and enrichment applications. Although Large Language Models (LLMs) have shown State-of-The-Art…
arXiv:2502.15131v3 Announce Type: replace-cross Abstract: We study the fundamental problem of calibrating a linear binary classifier of the form $sigma(hat{w}^top x)$, where the feature vector $x$ is Gaussian, $sigma$ is a link function, and $hat{w}$ is an estimator of the…
arXiv:2510.01268v1 Announce Type: cross Abstract: We study the problem of determining whether a piece of text has been authored by a human or by a large language model (LLM). Existing state of the art logits-based detectors make use of statistics…
arXiv:2510.02149v1 Announce Type: cross Abstract: We study reinforcement learning problems where state observations are stochastically triggered by actions, a constraint common in many real-world applications. This framework is formulated as Action-Triggered Sporadically Traceable Markov Decision Processes (ATST-MDPs), where each action…
arXiv:2510.02216v1 Announce Type: cross Abstract: Imputation methods play a critical role in enhancing the quality of practical time-series data, which often suffer from pervasive missing values. Recently, diffusion-based generative imputation methods have demonstrated remarkable success compared to autoregressive and conventional…
arXiv:2510.02239v1 Announce Type: cross Abstract: Conventional wisdom in deep learning optimization dictates updating all layers at every step-a principle followed by all recent state-of-the-art optimizers such as Muon. In this work, we challenge this assumption, showing that full-network updates can…
