Results 51 to 60 of about 224,776 (267)

Artificial Intelligence as the Next Visionary in Liquid Crystal Research

open access: yesAdvanced Functional Materials, EarlyView.
The functions of AI in the research laboratory are becoming increasingly sophisticated, allowing the entire process of hypothesis formulation, material design, synthesis, experimental design, and reiterative testing to be automated. In our work, we conceive how the incorporation of AI in the laboratory environment will transform the role and ...
Mert O. Astam   +2 more
wiley   +1 more source

Bayesian Neural Networks via MCMC: A Python-Based Tutorial

open access: yesIEEE Access
Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain Monte-Carlo (MCMC) sampling methods are used to implement Bayesian ...
Rohitash Chandra, Joshua Simmons
doaj   +1 more source

A Bayesian Deep Learning Approach With Convolutional Feature Engineering to Discriminate Cyber-Physical Intrusions in Smart Grid Systems

open access: yesIEEE Access, 2023
The emergence of cyber-physical smart grid (CPSG) systems has revolutionized the traditional power grid by enabling the bidirectional energy flow between consumers and utilities.
Devinder Kaur   +5 more
doaj   +1 more source

Beyond Presumptions: Toward Mechanistic Clarity in Metal‐Free Carbon Catalysts for Electrochemical H2O2 Production via Data Science

open access: yesAdvanced Materials, EarlyView.
Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu   +3 more
wiley   +1 more source

Triple equivalence for the emergence of biological intelligence

open access: yesCommunications Physics
Intelligent algorithms developed evolutionarily within neural systems are considered in this work. Mathematical analyses unveil a triple equivalence between canonical neural networks, variational Bayesian inference under a class of partially observable ...
Takuya Isomura
doaj   +1 more source

Probabilistic classification of quality of service in wireless computer networks

open access: yesICT Express, 2019
There is an increasing reliance on wireless computer networks for communicating various types of time sensitive applications such as voice over internet protocol (VoIP). Quality of service (QoS) can play an important role in wireless computer networks as
Abdussalam Salama, Reza Saatchi
doaj   +1 more source

Bayesian Semi-supervised Learning with Graph Gaussian Processes [PDF]

open access: yes, 2018
We propose a data-efficient Gaussian process-based Bayesian approach to the semi-supervised learning problem on graphs. The proposed model shows extremely competitive performance when compared to the state-of-the-art graph neural networks on semi ...
Colombo, Nicolo   +2 more
core   +1 more source

Bayesian Recurrent Neural Networks

open access: yes, 2017
In this work we explore a straightforward variational Bayes scheme for Recurrent Neural Networks. Firstly, we show that a simple adaptation of truncated backpropagation through time can yield good quality uncertainty estimates and superior regularisation at only a small extra computational cost during training, also reducing the amount of parameters by
Fortunato, Meire   +2 more
openaire   +2 more sources

Active Learning‐Guided Accelerated Discovery of Ultra‐Efficient High‐Entropy Thermoelectrics

open access: yesAdvanced Materials, EarlyView.
An active learning framework is introduced for the accelerated discovery of high‐entropy chalcogenides with superior thermoelectric performance. Only 80 targeted syntheses, selected from 16206 possible combinations, led to three high‐performance compositions, demonstrating the remarkable efficiency of data‐driven guidance in experimental materials ...
Hanhwi Jang   +8 more
wiley   +1 more source

Bayesian non-linear matching of pairwise microarray gene expressions [PDF]

open access: yes, 2008
In this paper, we present a Bayesian non-linear model to analyze matching pairs of microarray expression data. This model generalizes, in terms of neural networks, standard linear matching models. As a practical application, we analyze data of patients
Marín Díazaraque, Juan Miguel   +1 more
core   +1 more source

Home - About - Disclaimer - Privacy