Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Praneeth Vepakomma"'
Autor:
Gharib Gharibi, Ravi Patel, Anissa Khan, Babak Poorebrahim Gilkalaye, Praneeth Vepakomma, Ramesh Raskar, Steve Penrod, Greg Storm, Riddhiman Das
Publikováno v:
2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::55153afd106652d7461e951e8fc7b63e
https://doi.org/10.1109/icdcs54860.2022.00142
https://doi.org/10.1109/icdcs54860.2022.00142
Autor:
Seungeun Oh, Jihong Park, Praneeth Vepakomma, Sihun Baek, Ramesh Raskar, Mehdi Bennis, Seong-Lyun Kim
Publikováno v:
Proceedings of the ACM Web Conference 2022.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bd052f2908d105acfaf30f771c512805
https://doi.org/10.1145/3485447.3512153
https://doi.org/10.1145/3485447.3512153
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031197772
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::16c9d27a549f5653aa25e42b6eb937cf
https://doi.org/10.1007/978-3-031-19778-9_29
https://doi.org/10.1007/978-3-031-19778-9_29
Autor:
Praneeth Vepakomma, Ramesh Raskar
Publikováno v:
Federated Learning ISBN: 9783030968953
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9e7f34be09f8c50f91d0953baf2b535d
https://doi.org/10.1007/978-3-030-96896-0_19
https://doi.org/10.1007/978-3-030-96896-0_19
Publikováno v:
2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::784022e829eba16ba7e2a9ece8798e2e
https://doi.org/10.1109/fg52635.2021.9667085
https://doi.org/10.1109/fg52635.2021.9667085
Publikováno v:
2021 IEEE Global Communications Conference (GLOBECOM).
Wireless channels can be inherently privacy-preserving by distorting the received signals due to channel noise, and superpositioning multiple signals over-the-air. By harnessing these natural distortions and superpositions by wireless channels, we pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::db669092a180db7e8e05dab3bd235de1
https://doi.org/10.1109/globecom46510.2021.9685232
https://doi.org/10.1109/globecom46510.2021.9685232
Publikováno v:
arXiv
ICDM (Workshops)
ICDM (Workshops)
For distributed machine learning with sensitive data, we demonstrate how minimizing distance correlation between raw data and intermediary representations reduces leakage of sensitive raw data patterns across client communications while maintaining m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1b6940399859eba6aa39fab471407733
https://hdl.handle.net/1721.1/137081
https://hdl.handle.net/1721.1/137081
Autor:
Yulia Kempner, Praneeth Vepakomma
Publikováno v:
Discrete Applied Mathematics. 265:182-191
In this paper we show that the negative sample distance covariance function is a quasi-concave set function of samples of random variables that are not statistically independent. We use these properties to propose greedy algorithms to combinatorially
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::537064acdcf4e98d3dc376a91a8ada85
https://doi.org/10.1016/j.dam.2019.02.008
https://doi.org/10.1016/j.dam.2019.02.008
Autor:
Jayashree Kalpathy-Cramer, Maarten G. Poirot, Ramesh Raskar, Ken Chang, Praneeth Vepakomma, Daniel L. Rubin, Praveer Singh
Publikováno v:
Artificial Intelligence in Medicine ISBN: 9780128212592
Although deep learning models have great promise for clinical applications, there are numerous obstacles to training effective deep learning models. There is a need for collecting large quantities of diverse data, which often can only be achieved thr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fe21d09f2ade511bdd62b77028e69873
https://doi.org/10.1016/b978-0-12-821259-2.00006-5
https://doi.org/10.1016/b978-0-12-821259-2.00006-5
Autor:
Jeffery B. Alvarez, Jean-Emmanuel Bibault, Anita Burgun, Jinzheng Cai, Zhidong Cao, Ken Chang, Jonathan H. Chen, William C. Chen, Mildred Cho, Peter Jaeho Cho, Toby C. Cornish, Anthony Costa, Andre Dekker, Karen Drukker, Jessilyn Dunn, Okyaz Eminaga, Bradley J. Erickson, Laure Fournier, Sanjiv Sam Gambhir, Efstathios D. Gennatas, Maryellen L. Giger, Iva Halilaj, Adam P. Harrison, Bryan He, Julian C. Hong, Dakai Jin, Michael C. Jin, Arthur Jochems, Jayashree Kalpathy-Cramer, Daniel S. Kapp, Mehran Karimzadeh, William Karnes, Philippe Lambin, Curtis P. Langlotz, Joonsang Lee, Hui Li, Joseph C. Liao, Anthony L. Lin, Rebecca Y. Lin, Yun Liu, Le Lu, David Magnus, Chris McIntosh, Shun Miao, James K. Min, Daniel B. Neill, Eric Karl Oermann, David Ouyang, Lily Peng, Sonia Phene, Maarten G. Poirot, Jennifer L. Quon, Daniel Ranti, Arvind Rao, Ramesh Raskar, Christopher Rombaoa, Daniel L. Rubin, Jason Samarasena, Jayne Seekins, Karthik Seetharam, Emily Shearer, Adam Sibley, Karnika Singh, Praveer Singh, Margarita Sordo, Duminda Suraweera, Aly Al-Amyn Valliani, Yvonka van Wijk, Praneeth Vepakomma, Bo Wang, Ge Wang, Nicholas Wang, Yirui Wang, Elisa Warner, Mattea Welch, Kimberly Wong, Zhenqin Wu, Fuyong Xing, Lei Xing, Ke Yan, Pingkun Yan, Lu Yang, Kristen W. Yeom, Robin Zachariah, Daniel Zeng, Lin Zhang, Ling Zhang, Xuhong Zhang, Li Zhou, James Zou
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aa320ae1c368b21aa83be70ba954784f
https://doi.org/10.1016/b978-0-12-821259-2.00035-1
https://doi.org/10.1016/b978-0-12-821259-2.00035-1