Optimisation

AI Agents for Supply Chain Optimisation: Production Planning

, when combined with analytics products, can transform into powerful tools for supply chain optimisation. This quote is from a plant director who contacted us for the implementation of an algorithm to enhance their Master...

Denormalisation: Thoughtful Optimisation or Irrational Avant-Garde?

Perspective on Performance Optimisation and Data QualityProceed reading on Towards Data Science »

Introduction to Mathematical Optimisation in Python

Data Science FundamentalsBeginner’s practical guide to discrete optimisation in PythonData Scientists tackle a big selection of real-life problems using data and various techniques. Mathematical optimisation, a robust technique that could be applied to a...

Optimisation Algorithms: Neural Networks 101

How you can improve training beyond the “vanilla” gradient descent algorithmIn my last post, we discussed how you possibly can improve the performance of neural networks through hyperparameter tuning:This can be a process whereby...

Empowering Efficient BO Transfer with Neural Acquisition Process (NAP) General Objectives & Results: From Bayesian Optimisation to Meta-Bayesian Optimisation: Neural Acquisition Processes (NAP): Cool Properties:

Our primary objective is to reinforce the effectiveness of Bayesian Optimisation (BO) by leveraging meta-learning to transfer knowledge across different problem domains, thereby significantly improving sample efficiency.In pursuit of this goal, we introduce the...

Empowering Efficient BO Transfer with Neural Acquisition Process (NAP) General Objectives & Results: From Bayesian Optimisation to Meta-Bayesian Optimisation: Neural Acquisition Processes (NAP): Cool Properties:

Our primary objective is to boost the effectiveness of Bayesian Optimisation (BO) by leveraging meta-learning to transfer knowledge across different problem domains, thereby significantly improving sample efficiency.In pursuit of this goal, we introduce the...

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