Scientists are racing against time to try and create revolutionary, sustainable energy sources (such as solid-state batteries ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of Nadaraya-Watson kernel regression ...

Predictive ML shifts fulfillment from guesswork to data-driven precision, turning a major challenge into a competitive advantage.

In this article, we develop piecewise linear surrogates using Machine Learning (ML) models and the Optimization ... As process engineers, when we consider designing multiple instances of a particular ...

The analysis of multi-environment trials (MET) data in plant breeding and agricultural research is inherently challenging, with conventional ANOVA-based methods exhibiting limitations as the ...

To address these problems, we propose a self-weighted multi-view fuzzy clustering algorithm that incorporates multiple graph learning. Specifically, we automatically allocate weights corresponding to ...

Multi-layer machine learning-based diagnostic models were developed by leveraging motif-based feature and deep learning-based feature extraction using ProteinBERT from the 100 most abundant CDR3 ...

These factors have exposed limitations in traditional optimization strategies, which rely on linear or mixed-integer programming ... widespread adoption of reinforcement learning techniques, ...

It offers: A standardized interface to increase reproducibility Reduces boilerplate Automatic accumulation over batches Metrics optimized for distributed-training Automatic synchronization between ...

Mathematical & Statistical topics to perform statistical analysis and tests; Linear Regression, Probability Theory, Monte Carlo Simulation, Statistical Sampling, Bootstrapping, Dimensionality ...