arXiv:2504.19530v2 Announce Type: replace Abstract: This paper proposes and analyzes a gradient-type algorithm based on Burer-Monteiro factorization, called the Asymmetric Projected Gradient Descent (APGD), for reconstructing the point set configuration from partial Euclidean distance measurements, known as the Euclidean Distance…
arXiv:2510.15217v1 Announce Type: new Abstract: The 6th Annual Conference on Health, Inference, and Learning (CHIL 2025), hosted by the Association for Health Learning and Inference (AHLI), was held in person on June 25-27, 2025, at the University of California, Berkeley,…
arXiv:2506.09080v2 Announce Type: replace Abstract: Financial decision-making presents unique challenges for language models, demanding temporal reasoning, adaptive risk assessment, and responsiveness to dynamic events. While large language models (LLMs) show strong general reasoning capabilities, they often fail to capture behavioral…
arXiv:2510.15218v1 Announce Type: new Abstract: We utilized a cohort of 214 meningitis patients and 46,303 non-meningitis patients from the MIMIC-III database. After extensive data preprocessing, which included ICD-based cohort selection, one-hot encoding of coding, and a two-stage feature selection process…
arXiv:2508.00643v2 Announce Type: replace Abstract: Operator learning is a powerful paradigm for solving partial differential equations, with Fourier Neural Operators serving as a widely adopted foundation. However, FNOs face significant scalability challenges due to overparameterization and offer no native uncertainty…
arXiv:2510.15219v1 Announce Type: new Abstract: This work extends our previous study on enhancing 3D LiDAR point-cloud classification with product coefficients cite{medina2025integratingproductcoefficientsimproved}, measure-theoretic descriptors that complement the original spatial Lidar features. Here, we show that combining product coefficients with an autoencoder…
arXiv:2510.13865v2 Announce Type: replace-cross Abstract: We introduce the Deep Edge Filter, a novel approach that applies high-pass filtering to deep neural network features to improve model generalizability. Our method is motivated by our hypothesis that neural networks encode task-relevant semantic…
arXiv:2503.21473v2 Announce Type: replace-cross Abstract: Gaussian Processes (GPs) provide a flexible and statistically principled foundation for modelling spatiotemporal phenomena, but their $O(N^3)$ scaling makes them intractable for large datasets. Approximate methods such as variational inference (VI), inducing points (sparse GPs),…
arXiv:2510.12328v3 Announce Type: replace Abstract: Accurate rainfall forecasting, particularly for extreme events, remains a significant challenge in climatology and the Earth system. This paper presents novel physics-informed Graph Neural Networks (GNNs) combined with extreme-value analysis techniques to improve gauge-station rainfall…
arXiv:2504.00010v3 Announce Type: replace Abstract: Text-to-image (T2I) generation has made remarkable progress, yet existing systems still lack intuitive control over spatial composition, object consistency, and multi-step editing. We present $textbf{LayerCraft}$, a modular framework that uses large language models (LLMs) as…
