GitHub

mohansree14/Bayesian-Deep-Learning-for-Probabilistic-Financial-Forecasting

This project implements state-of-the-art deep learning models for financial time series forecasting with a focus on uncertainty quantification. The system provides not just point predictions, but ...
Oak Ridge National Lab

DeepHyper: A Python Package for Massively Parallel Hyperparameter Optimization in Machine Learning...

Machine learning models are increasingly applied across scientific disciplines, yet their effectiveness often hinges on heuristic decisions such as data transformations, training strategies, and model ...
The New York Times

Google’s Quantum Computer Makes a Big Technical Leap

Designed to accelerate advances in medicine and other fields, the tech giant’s quantum algorithm runs 13,000 times as fast as software written for a traditional supercomputer. A quantum computer at ...
GitHub

An AutoML framework for classical machine learning algorithms, automating model selection and hyperparameter tuning through Bayesian optimization, portfolio-based meta-learning ...

Start a Ray head node Connect and start Ray worker nodes via SSH Activate virtual environments and configure PYTHONPATH on all nodes 📌 Before running the script, ensure passwordless SSH access from ...
Frontiers

Federated learning framework for IoT intrusion detection using tab transformer and nature-inspired hyperparameter optimization

Intrusion detection has been of prime concern in the Internet of Things (IoT) environment due to the rapid increase in cyber threats. Majority of traditional intrusion detection systems (IDSs) rely on ...
Frontiers

Using a seasonal and trend decomposition algorithm to improve machine learning prediction of inflow from the Yellow River, China, into the sea

The Yellow River is the largest inflow into the Bohai Sea, and its inflow changes directly affect the ecological environment and marine health of the Bohai Sea. Therefore, accurate prediction of the ...
Scientific Research Publishing

Asif, D., Bibi, M., Arif, M.S. and Mukheimer, A. (2023) Enhancing Heart Disease Prediction through Ensemble Learning Techniques with Hyperparameter Optimization. Algorithms, 16 ...

ABSTRACT: Heart disease continues to be a major global cause of death, making the development of reliable prediction models necessary to enable early detection and treatment. Using machine learning to ...
Scientific Research Publishing

Yang, L. and Shami, A. (2020) On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice. Neurocomputing, 415, 295-316.

ABSTRACT: This paper focuses on the use of YOLOv12 for the early detection of Sexually Transmitted Infections, which are a global public health challenge. YOLOv12 is a deep-learning model released on ...
IEEE

Application and Effectiveness Evaluation of Bayesian Optimization Algorithm in Hyperparameter Tuning of Machine Learning Models

Abstract: Hyperparameter tuning is a crucial step in the development of machine learning models, as it directly impacts their performance and generalization ability. Traditional methods for ...