This model is part of the paper "Representation learning for multi-modal spatially resolved transcriptomics data". Authors: Kalin Nonchev, Sonali Andani, Joanna Ficek-Pascual, Marta Nowak, Bettina ...

Researchers have developed an artificial intelligence model that predicts crime more accurately than several existing ...

Autoencoders are a class of unsupervised neural networks designed to learn efficient data representations by encoding inputs into a compact latent space and then reconstructing them. Their versatility ...

To get an inside look at the heart, cardiologists often use electrocardiograms (ECGs) to trace its electrical activity and magnetic resonance images (MRIs) to map its structure. Because the two types ...

The Perspective by Tiwary et al. (8) offers a comprehensive overview of generative AI methods in computational chemistry. Approaches that generate new outputs (e.g., inferring phase transitions) by ...

The acquisition sites include: CALTECH, California Institute of Technology; CMU, Carnegie Mellon University; KKI, Kennedy Krieger Institute; LEUVEN, University of Leuven; MAX, Ludwig Maximilians ...

Several domains increasingly rely on machine learning in their applications. The resulting heavy dependence on data has led to the emergence of various laws and regulations around data ethics and ...

Abstract: Several domains increasingly rely on machine learning in their applications. The resulting heavy dependence on data has led to the emergence of various laws and regulations around data ...

Electric Vehicle (EV) cost prediction involves analyzing complex, high-dimensional data that often contains noise, multicollinearity, and irrelevant features. Traditional regression models struggle to ...

ABSTRACT: Predicting molecular properties is essential for advancing for advancing drug discovery and design. Recently, Graph Neural Networks (GNNs) have gained prominence due to their ability to ...