Researchers have developed an advanced artificial intelligence (AI) framework designed to significantly improve the forecasting of carbon dioxide emissions in the aviation sector. ACGRIME is an ...

However, inconsistent travel times and unpredictable congestion continue to undermine service reliability, particularly in ...

According to @godofprompt, a widespread trend in artificial intelligence research involves systematic p-hacking, where experiments are repeatedly run until benchmarks show improvement, with successes ...

Abstract: Hyperparameter tuning, such as learning rate decay and defining a stopping criterion, often relies on monitoring the validation loss. This paper presents NeVe, a dynamic training approach ...

Hyperparameter tuning is critical to the success of cross-device federated learning applications. Unfortunately, federated networks face issues of scale, heterogeneity, and privacy; addressing these ...

Abstract: Software defect prediction (SDP) is crucial for delivering high-quality software products. The SDP activities help software teams better utilize their software quality assurance efforts, ...

I've been working with the code and noticed that the current model (RandomForestRegressor) could benefit from hyperparameter tuning. The current setup uses default parameters, which may not be optimal ...

I am doing hyperparameter tuning for pose estimation using the model.tune method (not using RayTune). It was my understanding that fitness values ranged from 0 - 1 when tuning hyperparameters. My ...

Supervised Fine-Tuning (SFT) is a standard technique for adapting LLMs to new tasks by training them on expert demonstration datasets. It is valued for its simplicity and ability to develop ...

AutoML for Embedded, developed by Analog Devices (ADI) and Antmicro, is an open-source plugin for Visual Studio Code that works alongside ADI’s CodeFusion Studio plugin. Built on the Kenning framework ...