How does AADC compare to TensorFlow for CPU performance?

For problems requiring up to 1,000 inputs, AADC significantly outperforms TensorFlow and other Python-based tools on CPUs. This is particularly relevant for quantitative finance applications where model sizes are moderate but computational speed is critical.

Can I integrate custom neural network layers with my C++ quantitative models?

Yes, AADC allows you to seamlessly interleave machine learning with business analytics in C++. You can efficiently differentiate custom function definitions and create custom layers that integrate naturally with your existing codebase.

How can AAD help with Monte Carlo model calibration?

AADC enables Monte Carlo-based calibration of complex multi-asset models without requiring inflexible analytical approximations. You can calibrate models directly using simulation while efficiently computing gradients for optimization.

What are the benefits for time series analysis in finance?

Automated custom design of neural nets for time series provides up to 3x better accuracy than cutting-edge methods, with several times lower training time due to MatLogica's technology. This is ideal for financial forecasting and trading applications.

MatLogica | Machine Learning

High-Performance Machine Learning for Quantitative Finance

With MatLogica, both training and inference show significant speed-ups on CPUs, enabling seamless integration of ML into production quantitative systems without GPU dependencies.

For problems requiring up to 1,000 inputs, AADC significantly outperforms Python, TensorFlow, and other frameworks on CPU. Python-based tools are starting to catch up for very large problems (such as computer vision and linguistics) where performance is limited by memory bandwidth. See the ADBench benchmark results below:

ADBench Performance Comparison: AADC vs TensorFlow vs PyTorch

Figure: AADC performance comparison on CPU showing significant advantages for models with up to 1,000 inputs

State-of-the-Art Neural Networks for Time Series

Together with our partners, we offer automated custom design of neural networks for time series problems. This approach often provides accuracies that are much better than any of the standard neural network layers in TensorFlow.

Key Results:

Up to 3x more accurate than available cutting-edge methods
Several times faster training due to MatLogica's AAD technology
Ideal for financial forecasting, trading signals, and risk modeling

Download the paper produced by Prof. Roland Olsson and his team to learn about state-of-the-art neural network architectures for time series analysis.

Contact us at info@matlogica.com to find out more!

Monte Carlo Model Calibration

Easily calibrate complex multi-asset models using Monte Carlo simulation and avoid inflexible analytical approximations. AADC computes gradients efficiently for direct optimization.

Real-Time Model Recalibration

Use real-time market data to recalibrate your models continuously and maintain accurate, up-to-date risk assessments and pricing models.

Custom Neural Network Layers

Efficiently differentiate custom function definitions and integrate them seamlessly into your C++ codebase as if they were built-in from the start.

Use Cases for Quantitative Finance

Option Pricing Model Calibration: Calibrate Heston, SABR, and local volatility models using Monte Carlo with automatic gradients
Trading Signal Generation: Train neural networks on market data for alpha generation with faster training cycles
Credit Risk Modeling: Build custom ML models for default prediction integrated with existing C++ risk systems
Market Impact Modeling: Combine machine learning with transaction cost analysis in unified C++ framework
Portfolio Optimization: Use ML for return prediction while maintaining differentiable optimization

Related topics: algorithmic differentiation machine learning, C++ neural networks finance, TensorFlow alternative CPU, Monte Carlo calibration AAD, custom ML layers quantitative finance, financial time series neural networks, real-time model calibration, gradient-based optimization finance, AAD vs backpropagation, high-performance ML C++

Machine Learning

Custom Design of Neural Nets in C++

High-Performance Machine Learning for Quantitative Finance

State-of-the-Art Neural Networks for Time Series

Monte Carlo Model Calibration

Real-Time Model Recalibration

Custom Neural Network Layers

Use Cases for Quantitative Finance

Use Cases

About MatLogica

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