Physical Address
304 North Cardinal St.
Dorchester Center, MA 02124
AI Blogs
Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds Authors: Didong Li, Wenpin Tang, Sudipto Banerjee; Volume 24(101):1−26, 2023. Abstract Gaussian processes are widely used as versatile modeling and predictive tools in spatial statistics, functional data analysis, computer…
The field of science is currently facing a crisis in terms of reproducibility. One potential solution that has been proposed is the incorporation of data analysis replications into classrooms. However, the feasibility of this approach and what stakeholders can expect…
Variational Inference for Deblending Crowded Starfields Authors: Runjing Liu, Jon D. McAuliffe, Jeffrey Regier; Published in 2023, Volume 24(179):1−36 Abstract In astronomical survey images, it is common for stars and galaxies to visually overlap. Deblending refers to the task of…
The introduction of latent diffusers has brought about a revolution in generative AI and has served as a source of inspiration for creative art. By denoising the latent, the predicted original image progressively brings to life the formation process. However,…
Sparse Training with Lipschitz Continuous Loss Functions and a Weighted Group L0-norm Constraint Michael R. Metel; 24(103):1−44, 2023. Abstract This research paper focuses on the application of structured sparsity in deep neural network training. The study explores the utilization of…
The content can be rewritten as follows: Many affordable 3D scanners have a drawback of producing point clouds that are sparse and non-uniform. This can negatively affect the performance of robotic systems in downstream applications. Although existing point cloud upsampling…
Dropout Training: Distributional Robustness and Optimal Solution José Blanchet, Yang Kang, José Luis Montiel Olea, Viet Anh Nguyen, Xuhui Zhang; 24(180):1−60, 2023. Abstract This study demonstrates that dropout training in generalized linear models represents the minimax solution of a two-player,…
arXivLabs is a platform where collaborators can create and share new features for arXiv directly on our website. Both individuals and organizations that collaborate with arXivLabs share our values of openness, community, excellence, and user data privacy. We are committed…
Knowledge Hypergraph Embedding Meets Relational Algebra Authors: Bahare Fatemi, Perouz Taslakian, David Vazquez, David Poole; Volume 24, Issue 105, Pages 1-34, 2023. Abstract Relational databases have been successful in data storage and rely on query languages for information retrieval. These…
Introducing Point-TTA, a new framework for point cloud registration (PCR) that enhances the performance and generalization of registration models. Despite the impressive progress made by learning-based methods, adapting to unknown testing environments remains a challenge due to variations in 3D…
