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Mukund Sudarshan

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

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Schizophrenia Research|January 29, 2017
Deep dreaming, aberrant salience and psychosis: Connecting the dots by artificial neural networksMatcheri S Keshavan, Mukund Sudarshan
Advances in Neural Information Processing Systems|May 6, 2021
Deep direct likelihood knockoffsMukund Sudarshan, Wesley Tansey, Rajesh Ranganath
Proceedings of the National Academy of Sciences of the United States of America|October 5, 2023
Deciphering RNA splicing logic with interpretable machine learningSusan E Liao, Mukund Sudarshan, Oded Regev
Proceedings of Machine Learning Research|September 8, 2023
DIET: Conditional independence testing with marginal dependence measures of residual informationMukund Sudarshan, Aahlad Puli, Wesley Tansey, et al.
Proceedings of Machine Learning Research|May 6, 2021
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their InterpretationsNeil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs, et al.
Proceedings of Machine Learning Research|September 15, 2021
Contra: Contrarian statistics for controlled variable selectionMukund Sudarshan, Aahlad Puli, Lakshmi Subramanian, et al.
Nature Communications|August 15, 2023
Fast kernel-based association testing of non-linear genetic effects for biobank-scale dataBoyang Fu, Ali Pazokitoroudi, Mukund Sudarshan, et al.
Schizophrenia Research|May 30, 2018
Machine learning improved classification of psychoses using clinical and biological stratification: Update from the bipolar-schizophrenia network for intermediate phenotypes (B-SNIP)Suraj Sarvode Mothi, Mukund Sudarshan, Neeraj Tandon, et al.
Genes to Cells : Devoted to Molecular & Cellular Mechanisms|April 4, 2021
14-3-3γ prevents centrosome duplication by inhibiting NPM1 functionArunabha Bose, Kruti Modi, Suchismita Dey, et al.
European Heart Journal. Acute Cardiovascular Care|March 22, 2024
Development and external validation of a dynamic risk score for early prediction of cardiogenic shock in cardiac intensive care units using machine learningYuxuan Hu, Albert Lui, Mark Goldstein, et al.
Pageof 2

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

Sort By:
Pageof 2
Schizophrenia Research|January 29, 2017
Deep dreaming, aberrant salience and psychosis: Connecting the dots by artificial neural networksMatcheri S Keshavan, Mukund Sudarshan
Advances in Neural Information Processing Systems|May 6, 2021
Deep direct likelihood knockoffsMukund Sudarshan, Wesley Tansey, Rajesh Ranganath
Proceedings of the National Academy of Sciences of the United States of America|October 5, 2023
Deciphering RNA splicing logic with interpretable machine learningSusan E Liao, Mukund Sudarshan, Oded Regev
Proceedings of Machine Learning Research|September 8, 2023
DIET: Conditional independence testing with marginal dependence measures of residual informationMukund Sudarshan, Aahlad Puli, Wesley Tansey, et al.
Proceedings of Machine Learning Research|May 6, 2021
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their InterpretationsNeil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs, et al.
Proceedings of Machine Learning Research|September 15, 2021
Contra: Contrarian statistics for controlled variable selectionMukund Sudarshan, Aahlad Puli, Lakshmi Subramanian, et al.
Nature Communications|August 15, 2023
Fast kernel-based association testing of non-linear genetic effects for biobank-scale dataBoyang Fu, Ali Pazokitoroudi, Mukund Sudarshan, et al.
Schizophrenia Research|May 30, 2018
Machine learning improved classification of psychoses using clinical and biological stratification: Update from the bipolar-schizophrenia network for intermediate phenotypes (B-SNIP)Suraj Sarvode Mothi, Mukund Sudarshan, Neeraj Tandon, et al.
Genes to Cells : Devoted to Molecular & Cellular Mechanisms|April 4, 2021
14-3-3γ prevents centrosome duplication by inhibiting NPM1 functionArunabha Bose, Kruti Modi, Suchismita Dey, et al.
European Heart Journal. Acute Cardiovascular Care|March 22, 2024
Development and external validation of a dynamic risk score for early prediction of cardiogenic shock in cardiac intensive care units using machine learningYuxuan Hu, Albert Lui, Mark Goldstein, et al.
Pageof 2