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AI Blogs
Factor Graph Neural Networks Zhen Zhang, Mohammed Haroon Dupty, Fan Wu, Javen Qinfeng Shi, Wee Sun Lee; 24(181):1−54, 2023. Abstract In recent years, Graph Neural Networks (GNNs) have gained significant popularity and have achieved remarkable success in various real-world applications.…
The study focuses on dynamic motion generation tasks, such as contact and collisions, where small changes in policy parameters can have a significant impact on the outcomes. For instance, in soccer, even a slight variation in the hitting position, applied…
Risk Bounds for Positive-Unlabeled Learning Under the Selected At Random Assumption Olivier Coudray, Christine Keribin, Pascal Massart, Patrick Pamphile; 24(107):1−31, 2023. Abstract Positive-Unlabeled learning (PU learning) is a variant of semi-supervised binary classification where only a subset of positive examples…
Recently, there has been a growing interest in Cross-network node classification (CNNC) which involves classifying nodes in a target network with limited labels by leveraging knowledge from a source network with abundant labels. In order to tackle this problem, we…
Naive Regression vs. Factor Models: Adjusting for Multiple Cause Confounding Authors: Justin Grimmer, Dean Knox, Brandon Stewart; 24(182):1−70, 2023. Abstract Factor models are commonly used in various fields, such as genetics, networks, medicine, and politics, to account for shared, unobserved…
We introduce a novel approach for computing both ground and excited states of quantum systems. Our method utilizes a nonlinear variational framework and involves approximating wavefunctions using a linear combination of basis functions. These basis functions are enhanced and optimized…
Dimensionless Machine Learning: Enforcing Exact Units Equivariance Soledad Villar, Weichi Yao, David W. Hogg, Ben Blum-Smith, Bianca Dumitrascu; 24(109):1−32, 2023. Abstract Units equivariance (or units covariance) arises as an exact symmetry when the relationships among measured quantities of physics relevance…
arXivLabs provides a platform for collaborators to create and share new features on the arXiv website. Both individuals and organizations that collaborate with arXivLabs share our core values of openness, community, excellence, and user data privacy. We are dedicated to…
Quasi-Equivalence between Width and Depth of Neural Networks Fenglei Fan, Rongjie Lai, Ge Wang; 24(183):1−22, 2023. Abstract The classic studies have shown that wide networks have the ability to universally approximate, while recent advancements in deep learning have demonstrated the…
In this paper, we present a novel approach to memory in the least squares support vector machine (LSSVM). Our method introduces a memory influence mechanism that allows for accurate partitioning of the training set without overfitting, while maintaining the equation…
