Results 151 to 160 of about 6,917,983 (390)

Knowing what you know in brain segmentation using Bayesian deep neural networks

, 2019
In this paper, we describe a Bayesian deep neural network (DNN) for predicting FreeSurfer segmentations of structural MRI volumes, in minutes rather than hours.
Bandettini, Peter, Ghosh, Satrajit S., Kaczmarzyk, Jakub R., Lee, John A., McClure, Patrick, Nielson, Dylan, Pereira, Francisco, Rho, Nao, Thomas, Adam G., Zheng, Charles   +9 more
core  

Generative Inverse Design of Metamaterials with Functional Responses by Interpretable Learning

Advanced Intelligent Systems, EarlyView.
This work introduces random‐forest‐based interpretable generative inverse design (RIGID), a new single‐shot inverse design method for metamaterials using interpretable machine learning and Markov chain Monte Carlo sampling. Once trained on a small dataset, RIGID can estimate the likelihood of designs achieving target behaviors (e.g., wave‐based ...
Wei (Wayne) Chen, Rachel Sun, Doksoo Lee, Carlos M. Portela, Wei Chen   +4 more
wiley   +1 more source

Implementation of Continuous Bayesian Networks Using Sums of Weighted Gaussians [PDF]

arXiv, 2013
Bayesian networks provide a method of representing conditional independence between random variables and computing the probability distributions associated with these random variables. In this paper, we extend Bayesian network structures to compute probability density functions for continuous random variables.
arxiv  

Learning Bayesian networks: The combination of knowledge and statistical data [PDF]

, 1995
David Heckerman, Dan Geiger, David M. Chickering   +2 more
openalex   +1 more source

Sensorless Human–Robot Interaction: Real‐Time Estimation of Co‐Grasped Object Mass and Human Wrench for Compliant Interaction

Advanced Intelligent Systems, EarlyView.
To tackle the challenge of parameter estimation in dynamic human–robot interaction, this research proposes a novel method that integrates a disturbance observer with Kalman filtering. In contrast to conventional techniques, this approach facilitates the simultaneous real‐time estimation of human wrench and object parameters, offering significantly ...
Minghao Bai, Xizhe Zang, Lei Wu, Changle Li, Yanhe Zhu, Jie Zhao   +5 more
wiley   +1 more source

Canadian contributions to environmetrics

Canadian Journal of Statistics, Volume 50, Issue 4, Page 1355-1386, December 2022., 2022
Abstract This article focuses on the importance of collaboration in statistics by Canadian researchers and highlights the contributions that Canadian statisticians have made to many research areas in environmetrics. We provide a discussion about different vehicles that have been developed for collaboration by Canadians in the environmetrics context as ...
Charmaine B. Dean, Abdel H. El‐Shaarawi, Sylvia R. Esterby, Joanna Mills Flemming, Richard D. Routledge, Stephen W. Taylor, Douglas G. Woolford, James V. Zidek, Francis W. Zwiers   +8 more
wiley   +1 more source

Integrating Bayesian network and generalized raking for population synthesis in Greater Jakarta

Regional Studies, Regional Science, 2019
Constructing agent data with detailed information on their sociodemographics is substantially important for agent-based modelling. However, to collect data about the whole population is not efficient, since it requires an expensive and time-consuming ...
Anugrah Ilahi, Kay W. Axhausen
doaj   +1 more source

Using Deep Neural Network Approximate Bayesian Network [PDF]

arXiv, 2017
We present a new method to approximate posterior probabilities of Bayesian Network using Deep Neural Network. Experiment results on several public Bayesian Network datasets shows that Deep Neural Network is capable of learning joint probability distri- bution of Bayesian Network by learning from a few observation and posterior probability distribution ...
arxiv  

Machine Learning‐Assisted Simulations and Predictions for Battery Interfaces

Advanced Intelligent Systems, EarlyView.
This review summarizes machine learning (ML)‐assisted simulations and predictions at battery interfaces. It highlights how employing ML algorithms with machine vision, enables the lithium dendrite growth simulation, the solid–electrolyte interphase formation, and other interfacial dynamics.
Zhaojun Sun, Xin Li, Yiming Wu, Qilin Gu, Shiyou Zheng   +4 more
wiley   +1 more source

Recent Developments in Process Digitalisation for Advanced Nanomaterial Syntheses

Chemistry–Methods, Volume 2, Issue 12, December 2022., 2022
The combination of flow, computer‐controlled equipment and inline analytics enables the digitalisation of chemical syntheses. This works highlights how these tools can be applied in the development of advanced materials. Abstract Digitalisation and industry 4.0 are set to profoundly change the way chemical and materials discovery and development work ...
Diego Iglesias, Dina Haddad, Victor Sans
wiley   +1 more source