by instadatahelp | Sep 2, 2023 | AI Blogs
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...
![Normalizing Flows for Computing Excited States of Molecules (arXiv:2308.16468v1 [physics.chem-ph])](https://instadatanews.com/wp-content/uploads/2023/08/arxiv-logo-fb-1080x675.png)
by instadatahelp | Sep 2, 2023 | AI Blogs
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...
by instadatahelp | Sep 2, 2023 | AI Blogs
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...
by instadatahelp | Sep 2, 2023 | AI Blogs
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...
by instadatahelp | Sep 2, 2023 | AI Blogs
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...
by instadatahelp | Sep 2, 2023 | AI Blogs
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...
