Machine learning is transforming many scientific fields, including computational materials science. For about two decades, ...

Unsupervised, model-free method preserves key data better than traditional statistical techniques for next generation cognitive ML for multi-modal data. These methods prove the utility of algorithmic ...

Modern neural networks, with billions of parameters, are so overparameterized that they can "overfit" even random, ...

This diagram illustrates how the team reduces quantum circuit complexity in machine learning using three encoding methods—variational, genetic, and matrix product state algorithms. All methods ...

Deep learning models have shown great potential in predicting and engineering functional enzymes and proteins. Does this prowess extend to other fields of biology as well? Subscribe to our newsletter ...

Social determinants of health and disease complexity were both key factors influencing gaps in care for congenital heart ...

Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting? Based on our evaluation of all models from both classes ever used in ...

Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance metrics do not capture it. Consequently, a model might offer sufficient ...

AI black box models lack transparency, making investment decisions unclear. White box models are slower but clarify their decision-making processes. Investors should verify AI outputs to align with ...