With MatLogica, both training and inference show great speed-up on CPUs.
For problems requiring up to 1,000 inputs, AADC by far outperforms tools like Python, Tensorflow, and others. Python-based tools are starting to catch up for very large problems (such as computer vision and linguistics) where we are limited by the memory bandwidth. See the results of the ADBench benchmark below:
Monte-Carlo Based Calibration
You can easily calibrate complex multi-asset models relying on Monte-Carlo and avoid using inflexible and difficult to derive analytical approximations.
Recalibrate Your Models
Use real-time data to recalibrate your models and get an accurate and up-to-date information.
Create Custom Layers
Efficiently differentiate the custom function definitions as if they were there from the start.