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Babak Shahbaba

Showing results (21-30 of 55) with videos related to

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Bioinformatics (Oxford, England)|August 21, 2016
What time is it? Deep learning approaches for circadian rhythmsForest Agostinelli, Nicholas Ceglia, Babak Shahbaba, et al.
Journal of the American Statistical Association|August 11, 2025
A Model-Agnostic Graph Neural Network for Integrating Local and Global InformationWenzhuo Zhou, Annie Qu, Keiland W Cooper, et al.
Bioinformatics (Oxford, England)|June 17, 2016
What time is it? Deep learning approaches for circadian rhythmsForest Agostinelli, Nicholas Ceglia, Babak Shahbaba, et al.
Journal of the American Statistical Association|December 20, 2016
A Dynamic Bayesian Model for Characterizing Cross-Neuronal Interactions During Decision-MakingBo Zhou, David E Moorman, Sam Behseta, et al.
Statistica Sinica|August 11, 2020
Evolutionary State-Space Model and Its Application to Time-Frequency Analysis of Local Field PotentialsXu Gao, Weining Shen, Babak Shahbaba, et al.
Bioinformatics (Oxford, England)|June 21, 2015
An efficient Bayesian inference framework for coalescent-based nonparametric phylodynamicsShiwei Lan, Julia A Palacios, Michael Karcher, et al.
Plos One|April 25, 2013
Genetic variation in the human brain dopamine system influences motor learning and its modulation by L-DopaKristin M Pearson-Fuhrhop, Brian Minton, Daniel Acevedo, et al.
Advances in Neural Information Processing Systems|October 12, 2020
Modeling Dynamic Functional Connectivity with Latent Factor Gaussian ProcessesLingge Li, Dustin Pluta, Babak Shahbaba, et al.
Neural Computation|June 13, 2014
A semiparametric Bayesian model for detecting synchrony among multiple neuronsBabak Shahbaba, Bo Zhou, Shiwei Lan, et al.
Bayesian Analysis|April 19, 2021
Flexible Bayesian Dynamic Modeling of Correlation and Covariance MatricesShiwei Lan, Andrew Holbrook, Gabriel A Elias, et al.
Pageof 6

Showing results (21-30 of 55) with videos related to

Sort By:
Pageof 6
Bioinformatics (Oxford, England)|August 21, 2016
What time is it? Deep learning approaches for circadian rhythmsForest Agostinelli, Nicholas Ceglia, Babak Shahbaba, et al.
Journal of the American Statistical Association|August 11, 2025
A Model-Agnostic Graph Neural Network for Integrating Local and Global InformationWenzhuo Zhou, Annie Qu, Keiland W Cooper, et al.
Bioinformatics (Oxford, England)|June 17, 2016
What time is it? Deep learning approaches for circadian rhythmsForest Agostinelli, Nicholas Ceglia, Babak Shahbaba, et al.
Journal of the American Statistical Association|December 20, 2016
A Dynamic Bayesian Model for Characterizing Cross-Neuronal Interactions During Decision-MakingBo Zhou, David E Moorman, Sam Behseta, et al.
Statistica Sinica|August 11, 2020
Evolutionary State-Space Model and Its Application to Time-Frequency Analysis of Local Field PotentialsXu Gao, Weining Shen, Babak Shahbaba, et al.
Bioinformatics (Oxford, England)|June 21, 2015
An efficient Bayesian inference framework for coalescent-based nonparametric phylodynamicsShiwei Lan, Julia A Palacios, Michael Karcher, et al.
Plos One|April 25, 2013
Genetic variation in the human brain dopamine system influences motor learning and its modulation by L-DopaKristin M Pearson-Fuhrhop, Brian Minton, Daniel Acevedo, et al.
Advances in Neural Information Processing Systems|October 12, 2020
Modeling Dynamic Functional Connectivity with Latent Factor Gaussian ProcessesLingge Li, Dustin Pluta, Babak Shahbaba, et al.
Neural Computation|June 13, 2014
A semiparametric Bayesian model for detecting synchrony among multiple neuronsBabak Shahbaba, Bo Zhou, Shiwei Lan, et al.
Bayesian Analysis|April 19, 2021
Flexible Bayesian Dynamic Modeling of Correlation and Covariance MatricesShiwei Lan, Andrew Holbrook, Gabriel A Elias, et al.
Pageof 6