Researchers evaluated eight machine learning models on high-performance concrete mixtures, demonstrating that optimized ...

Researchers combined a low-cost IoT monitoring prototype with a Bayesian-optimized XGBoost model that achieved the highest accuracy and fastest computation time among tested machine learning models ...

When Numba code disappoints, it's nearly never the compiler, and usually ends up being the boundary around the compiled code: not crossing it, not making it wide enough, or crossing it during every ...

Token optimization is critical for designing precise prompts. A Python framework can be designed to accept an English prompt as input, analyse it, remove duplicate or low-value content, and return a ...

In this tutorial, we implement an advanced Bayesian hyperparameter optimization workflow using Hyperopt and the Tree-structured Parzen Estimator (TPE) algorithm. We construct a conditional search ...

In this tutorial, we build a complete, production-grade ML experimentation and deployment workflow using MLflow. We start by launching a dedicated MLflow Tracking Server with a structured backend and ...

Abstract: Interference-aware beamforming for millimeter wave multiple-input multiple-output (mmWave MIMO) systems is challenged by limited channel state information (CSI) availability and the ...

Tuning hyperparameters in machine learning models is, to some extent, an art or craftsmanship, requiring the right skills to balance experience, intuition, and plenty of experimentation. In practice, ...

Abstract: Hyperparameter optimization plays a pivotal role in the reliability and generalization of machine-learning models for software quality prediction. This paper presents a comparative ...

Vector search put a retired rule first. The current one came ninth. Build an on-prem AI agent that gets this right: filters inside the search, a local model, and memory that saves only what a person ...