Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Filters

Yee-Whye Teh

Showing results (1-10 of 11) with videos related to

Pageof 2
Sort By:
Neural Computation|March 10, 2004
Linear response algorithms for approximate inference in graphical modelsMax Welling, Yee Whye Teh
Biometrics|July 31, 2015
Rediscovery of Good-Turing estimators via Bayesian nonparametricsStefano Favaro, Bernardo Nipoti, Yee Whye Teh
Neural Computation|June 13, 2006
A fast learning algorithm for deep belief netsGeoffrey E Hinton, Simon Osindero, Yee-Whye Teh
Cognitive Science|June 28, 2011
Unsupervised discovery of nonlinear structure using contrastive backpropagationGeoffrey Hinton, Simon Osindero, Max Welling, et al.
IEEE Transactions on Pattern Analysis and Machine Intelligence|November 25, 2015
Guest Editors' Introduction to the Special Issue on Bayesian NonparametricsRyan P Adams, Emily B Fox, Erik B Sudderth, et al.
Royal Society Open Science|May 7, 2021
Effectiveness and resource requirements of test, trace and isolate strategies for COVID in the UKBobby He, Sheheryar Zaidi, Bryn Elesedy, et al.
Journal of the Royal Statistical Society. Series A, (Statistics in Society)|April 12, 2024
Efficient Bayesian inference of instantaneous reproduction numbers at fine spatial scales, with an application to mapping and nowcasting the Covid-19 epidemic in British local authoritiesYee Whye Teh, Bryn Elesedy, Bobby He, et al.
Nature Methods|October 13, 2020
DeepC: predicting 3D genome folding using megabase-scale transfer learningRon Schwessinger, Matthew Gosden, Damien Downes, et al.
Statistical Science : a Review Journal of the Institute of Mathematical Statistics|June 6, 2022
Interoperability of statistical models in pandemic preparedness: principles and realityGeorge Nicholson, Marta Blangiardo, Mark Briers, et al.
Science (New York, N.Y.)|December 16, 2020
Inferring the effectiveness of government interventions against COVID-19Jan M Brauner, Sören Mindermann, Mrinank Sharma, et al.
Pageof 2

Showing results (1-10 of 11) with videos related to

Sort By:
Pageof 2
Neural Computation|March 10, 2004
Linear response algorithms for approximate inference in graphical modelsMax Welling, Yee Whye Teh
Biometrics|July 31, 2015
Rediscovery of Good-Turing estimators via Bayesian nonparametricsStefano Favaro, Bernardo Nipoti, Yee Whye Teh
Neural Computation|June 13, 2006
A fast learning algorithm for deep belief netsGeoffrey E Hinton, Simon Osindero, Yee-Whye Teh
Cognitive Science|June 28, 2011
Unsupervised discovery of nonlinear structure using contrastive backpropagationGeoffrey Hinton, Simon Osindero, Max Welling, et al.
IEEE Transactions on Pattern Analysis and Machine Intelligence|November 25, 2015
Guest Editors' Introduction to the Special Issue on Bayesian NonparametricsRyan P Adams, Emily B Fox, Erik B Sudderth, et al.
Royal Society Open Science|May 7, 2021
Effectiveness and resource requirements of test, trace and isolate strategies for COVID in the UKBobby He, Sheheryar Zaidi, Bryn Elesedy, et al.
Journal of the Royal Statistical Society. Series A, (Statistics in Society)|April 12, 2024
Efficient Bayesian inference of instantaneous reproduction numbers at fine spatial scales, with an application to mapping and nowcasting the Covid-19 epidemic in British local authoritiesYee Whye Teh, Bryn Elesedy, Bobby He, et al.
Nature Methods|October 13, 2020
DeepC: predicting 3D genome folding using megabase-scale transfer learningRon Schwessinger, Matthew Gosden, Damien Downes, et al.
Statistical Science : a Review Journal of the Institute of Mathematical Statistics|June 6, 2022
Interoperability of statistical models in pandemic preparedness: principles and realityGeorge Nicholson, Marta Blangiardo, Mark Briers, et al.
Science (New York, N.Y.)|December 16, 2020
Inferring the effectiveness of government interventions against COVID-19Jan M Brauner, Sören Mindermann, Mrinank Sharma, et al.
Pageof 2