Ivy Zhu, Research Scientist, Intel at MLconf SEA - 5/01/15MLconf
Model-Based Machine Learning for Real-Time Brain Decoding: Neurofeedback derived from real-time functional magnetic resonance imaging (rtfMRI) is promising for both scientific applications, such as uncovering hidden brain networks that respond to stimulus, and clinical applications, such as helping people cope with brain disorders ranging from addiction to autism. One of the greatest challenges in applying machine learning to real time brain “decoding” is that traditional methods fit per-voxel parameters, leading to large computational problems on relatively small datasets. As such, it is easy to over-fit parameters to noise rather than the desired signals. Bayesian model-based hierarchical topographical factor analysis (HTFA) solves this problem by uncovering low-dimensional representations (latent factors) of brain images, fitting parameters for latent factors (rather than voxels) while removing the false assumption that all voxels are independent. In this talk, we’ll discuss the promise of using this and other model-based machine learning to better understand full-brain activity and functional connectivity. And we’ll show how Intel Labs and its partners are combining neuroscience and computer science expertise to further extend such algorithms for real-time brain decoding.
Ivy Zhu, Research Scientist, Intel at MLconf SEA - 5/01/15MLconf
Model-Based Machine Learning for Real-Time Brain Decoding: Neurofeedback derived from real-time functional magnetic resonance imaging (rtfMRI) is promising for both scientific applications, such as uncovering hidden brain networks that respond to stimulus, and clinical applications, such as helping people cope with brain disorders ranging from addiction to autism. One of the greatest challenges in applying machine learning to real time brain “decoding” is that traditional methods fit per-voxel parameters, leading to large computational problems on relatively small datasets. As such, it is easy to over-fit parameters to noise rather than the desired signals. Bayesian model-based hierarchical topographical factor analysis (HTFA) solves this problem by uncovering low-dimensional representations (latent factors) of brain images, fitting parameters for latent factors (rather than voxels) while removing the false assumption that all voxels are independent. In this talk, we’ll discuss the promise of using this and other model-based machine learning to better understand full-brain activity and functional connectivity. And we’ll show how Intel Labs and its partners are combining neuroscience and computer science expertise to further extend such algorithms for real-time brain decoding.
XIII Encontro Nacional da APEI - Associação de Profissionais de Educação de Infância que se realiza nos próximos dias 10 e 11 de julho no Auditório da Reitoria da Universidade de Coimbra.
Contamos com a sua presença. Inscrições em www.apei.pt
Wie kann ich XING als Unternehmer nutzen? Welche Möglichkeiten bietet mir das soziale Netzwerk? Peter Wode, offiziell lizenzierter XING-Trainer und Netzwerkpartner der medien-sprechstunde gibt eine kurze Einführung.
Xileno: contaminante detectado en suelo de zona JohnsonTERRATOX
HOJA CON UN RESUMEN DE RIESGOS SANITARIOS DEL CONTAMINANTE XILENO DETECTADO EN SUELO DE ZONA JOHNSON. (SITIO CONTAMINADO DE LA LOCALIDAD DE PABLO PODESTA, MUNICIPIO DE TRES DE FEBRERO, PROVINCIA DE BUENOS AIRES).Información aplicable a todo sitio contaminado (no exclusivamente zona Johnson).
Este contaminante junto a los otros 12 detectados en suelo forman parte de información anterior al año 2012 y contenida en documentos oficiales .
Se aguardan en la zona los nuevos resultados de los monitoreos de suelo y agua iniciados por el municipio de Tres de Febrero en el 2013 a partir de los planteos de TERRATOX:
1º: http://es.slideshare.net/TERRATOX/expediente-18756-t-2012
2º:
http://es.slideshare.net/TERRATOX/nota-056-13-y-anexos-hidrocarb-en-suelo-vivienda-contigua-a-sc-johnson