Tabular artificial intelligence startup Prior Labs GmbH today announced a new foundation model that can handle millions of rows of data to give enterprises a way to understand and use their most ...

Our research proves a conjecture from string theory asserting the vanishing of a specific convolution sum arising in the 4-graviton scattering amplitude in 10-dimensional type IIB string theory. The ...

Mitra departs from the norm by being pretrained exclusively on synthetic data. Rather than relying on the limited and heterogeneous nature of real-world tabular datasets, Amazon researchers engineered ...

ABSTRACT: With the rapid development of global economic integration and digital technology, the transportation system, as a core component of the supply chain, has become a key factor influencing the ...

1 School of Mathematics and Statistics, Sichuan University of Science and Engineering, Zigong, China. 2 Institute of Computational Mathematics and Scientific/Engineering Computing, Chinese Academy of ...

A team has developed a new method that facilitates and improves predictions of tabular data, especially for small data sets with fewer than 10,000 data points. The new AI model TabPFN is trained on ...

With this notice, Frontiers states its awareness of concerns regarding the content of the article “Short-time photovoltaic output prediction method based on depthwise separable convolution visual ...

One significant challenge in applying deep learning to tabular data is balancing model complexity and computational efficiency. Traditional machine learning methods, particularly gradient-boosted ...

A comprehensive toolkit and benchmark for tabular data learning, featuring 30 deep methods, more than 10 classical methods, and 300 diverse tabular datasets.

Abstract: Recently, convolution neural networks (CNNs) have attracted a great deal of attention due to their remarkable performance in various domains, particularly in image and text classification ...