Statistical models based on Gaussian random variables occupy a central position in modern data analysis, offering a mathematically tractable framework for inference, prediction and dimensionality ...
The generation of synthetic market data is widely seen as one of the most promising applications of sophisticated artificial intelligence models, such as generative adversarial networks (GANs) and ...
In recent times, there has been a huge amount of work on utilizing deep learning (DL) to estimate the quality of transmission (QoT) in optical networks. This research depict a lightpath’s quality of ...
Simulated prostate treatment plan in the simple heterogeneous phantom. (Courtesy: J. Appl. Clin. Med. Phys. 10.1002/acm2.12535/CC BY 4.0) A new treatment planning system (TPS) for proton therapy has ...
Dr. James McCaffrey of Microsoft Research says the main advantage of using Gaussian naive Bayes classification compared to other techniques like decision trees or neural networks is that you don't ...
Neural networks revolutionized machine learning for classical computers: self-driving cars, language translation and even artificial intelligence software were all made possible. It is no wonder, then ...
Despite growing interest in the use of complex models, such as machine learning (ML) models, for credit underwriting, ML models are difficult to interpret, and it is possible for them to learn ...
Swaptions and constant maturity swap spread options are essential to calibrating interest rate models yet remain computationally demanding. Toufik Bellaj, Khalid Bellaj and Hicham Nait Yahia propose a ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results