Distributed deep learning has emerged as an essential approach for training large-scale deep neural networks by utilising multiple computational nodes. This methodology partitions the workload either ...

As cities around the world continue to expand and evolve, understanding the dynamics of the housing market becomes increasingly critical for urban planners, ...

Researchers at EPFL have developed a deep-learning framework that dramatically improves vehicle re-identification in ...

Researchers have developed a hybrid surrogate model for iso-octanol oxidation to iso-octanal that integrates data-driven ...

A recent publication from IMDEA Materials Institute and the Technical University of Madrid (UPM) presents a major step ...

Of 372 patients studied, 79.3% and 20.7% were in the completion group and the non-completion group, respectively. The final BERT model achieved average F1 scores of 0.91 and 0.98 for time to ...

Physiologically Based Pharmacokinetic Model to Assess the Drug-Drug-Gene Interaction Potential of Belzutifan in Combination With Cyclin-Dependent Kinase 4/6 Inhibitors A total of 14,177 patients were ...

Factoring out nucleotide-level mutation biases from antibody language models dramatically improves prediction of functional mutation effects while reducing computational cost by orders of magnitude.

A model integrating deep learning with clinical and epidemiologic data may significantly improve lung cancer risk prediction based on LDCT screening.

Artificial intelligence (AI) and machine learning (ML) systems have become central to modern data-driven decision-making. They are now widely applied in fields as diverse as healthcare, finance, ...

Deep learning is a branch of machine learning based on algorithms that try to model high-level abstract representations of data by using multiple processing layers with complex structures. One of the ...