IEEE

Integrating Bayesian Optimization and Machine Learning for the Optimal Configuration of Cloud Systems

Abstract: Bayesian Optimization (BO) is an efficient method for finding optimal cloud configurations for several types of applications. On the other hand, Machine Learning (ML) can provide helpful ...
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 ...
GitHub

Bayesian machine learning notebooks

Bayesian regression with linear basis function models. Introduction to Bayesian linear regression. Implementation with plain NumPy and scikit-learn. See also PyMC3 implementation. Gaussian processes.
Nature

Bayesian optimization of biodegradable polymers via machine learning driven features from low-field NMR data

Effective designs of biodegradable polymers are highly desirable for achieving a sustainable society by decreasing environmental burden and replacing petroleum-based resources with biomass. Low-field ...
IEEE

Analysis of Gas Pipeline Failure Factors Based on the Novel Bayesian Network by Machine Learning Optimization

Abstract: Assessing the failure of urban gas pipelines is crucial for identifying risk factors and preventing gas accidents that result in economic losses and casualties. Most previous studies on gas ...
Science Daily

Strong as steel, light as foam: High-performance, nano-architected materials

Researchers have used machine learning to design nano-architected materials that have the strength of carbon steel but the lightness of Styrofoam. The team describes how they made nanomaterials with ...
Nature

Bayesian optimization with Gaussian-process-based active machine learning for improvement of geometric accuracy in projection multi-photon 3D printing

Advancements in additive manufacturing (AM) have transformed the manufacturing industry. The freedom of design, rapid prototyping, and low waste offered by AM, also called 3D printing, have led to its ...
Frontiers

Geostatistics and artificial intelligence coupling: advanced machine learning neural network regressor for experimental variogram modelling using Bayesian optimization

Experimental variogram modelling is an essential process in geostatistics. The use of artificial intelligence (AI) is a new and advanced way of automating experimental variogram modelling. One part of ...
Frontiers

Advanced machine learning artificial neural network classifier for lithology identification using Bayesian optimization

Identifying lithology is crucial for geological exploration, and the adoption of artificial intelligence is progressively becoming a refined approach to automate this process. A key feature of this ...
Scientific Research Publishing

Hybrid Data-Driven and Deep Learning Based Portfolio Optimization ()

1 Department of Computer Science, University of Manitoba, Winnipeg, Canada. 2 Department of Statistics, University of Manitoba, Winnipeg, Canada. This research introduces a novel hybrid architecture ...
LinkedIn

Optimizing Neural Networks with Bayesian Optimization and Gaussian Processes

In the realm of machine learning, tuning a model to achieve optimal performance often involves navigating through a complex space of hyperparameters. One effective strategy for this is Bayesian ...