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

Daniel Kifer

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

Pageof 1
Sort By:
Nature Communications|April 20, 2022
The neural coding framework for learning generative modelsAlexander Ororbia, Daniel Kifer
Neural Computation|January 18, 2017
Unifying Adversarial Training Algorithms with Data Gradient RegularizationAlexander G Ororbia Ii, Daniel Kifer, C Lee Giles
IEEE Transactions on Neural Networks and Learning Systems|January 25, 2020
Continual Learning of Recurrent Neural Networks by Locally Aligning Distributed RepresentationsAlexander Ororbia, Ankur Mali, C Lee Giles, et al.
IEEE Transactions on Big Data|June 8, 2019
Non-Stationary Model for Crime Rate Inference Using Modern Urban DataHongjian Wang, Huaxiu Yao, Daniel Kifer, et al.
Nature Communications|June 21, 2023
Using a physics-informed neural network and fault zone acoustic monitoring to predict lab earthquakesPrabhav Borate, Jacques Rivière, Chris Marone, et al.
Scientific Reports|October 19, 2024
Physics informed neural network can retrieve rate and state friction parameters from acoustic monitoring of laboratory stick-slip experimentsPrabhav Borate, Jacques Rivière, Samson Marty, et al.
Justice Quarterly : JQ|May 24, 2021
Network spillovers and neighborhood crime: A computational statistics analysis of employment-based networks of neighborhoodsCorina Graif, Brittany N Freelin, Yu-Hsuan Kuo, et al.
Journal of Contaminant Hydrology|June 6, 2021
Physics-informed deep learning for prediction of CO<sub>2</sub> storage site responseParisa Shokouhi, Vikas Kumar, Sumedha Prathipati, et al.
Proceedings of the National Academy of Sciences of the United States of America|March 5, 2024
Reply to Muralidhar et al., Kenny et al., and Hotz et al.: The benefits of engagement with external research teamsRon S Jarmin, John M Abowd, Robert Ashmead, et al.
Proceedings of the National Academy of Sciences of the United States of America|October 13, 2023
An in-depth examination of requirements for disclosure risk assessmentRon S Jarmin, John M Abowd, Robert Ashmead, et al.
Pageof 1

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

Sort By:
Pageof 1
Nature Communications|April 20, 2022
The neural coding framework for learning generative modelsAlexander Ororbia, Daniel Kifer
Neural Computation|January 18, 2017
Unifying Adversarial Training Algorithms with Data Gradient RegularizationAlexander G Ororbia Ii, Daniel Kifer, C Lee Giles
IEEE Transactions on Neural Networks and Learning Systems|January 25, 2020
Continual Learning of Recurrent Neural Networks by Locally Aligning Distributed RepresentationsAlexander Ororbia, Ankur Mali, C Lee Giles, et al.
IEEE Transactions on Big Data|June 8, 2019
Non-Stationary Model for Crime Rate Inference Using Modern Urban DataHongjian Wang, Huaxiu Yao, Daniel Kifer, et al.
Nature Communications|June 21, 2023
Using a physics-informed neural network and fault zone acoustic monitoring to predict lab earthquakesPrabhav Borate, Jacques Rivière, Chris Marone, et al.
Scientific Reports|October 19, 2024
Physics informed neural network can retrieve rate and state friction parameters from acoustic monitoring of laboratory stick-slip experimentsPrabhav Borate, Jacques Rivière, Samson Marty, et al.
Justice Quarterly : JQ|May 24, 2021
Network spillovers and neighborhood crime: A computational statistics analysis of employment-based networks of neighborhoodsCorina Graif, Brittany N Freelin, Yu-Hsuan Kuo, et al.
Journal of Contaminant Hydrology|June 6, 2021
Physics-informed deep learning for prediction of CO<sub>2</sub> storage site responseParisa Shokouhi, Vikas Kumar, Sumedha Prathipati, et al.
Proceedings of the National Academy of Sciences of the United States of America|March 5, 2024
Reply to Muralidhar et al., Kenny et al., and Hotz et al.: The benefits of engagement with external research teamsRon S Jarmin, John M Abowd, Robert Ashmead, et al.
Proceedings of the National Academy of Sciences of the United States of America|October 13, 2023
An in-depth examination of requirements for disclosure risk assessmentRon S Jarmin, John M Abowd, Robert Ashmead, et al.
Pageof 1