Praneeth Vepakomma
Titel
Geciteerd door
Geciteerd door
Jaar
Advances and open problems in federated learning
P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ...
arXiv preprint arXiv:1912.04977, 2019
11612019
Split Learning for Health: Distributed Deep Learning Without Sharing Raw Patient Data
P Vepakomma, O Gupta, T Swedish, R Raskar
1722019
Apps gone rogue: Maintaining personal privacy in an epidemic
R Raskar, I Schunemann, R Barbar, K Vilcans, J Gray, P Vepakomma, ...
arXiv preprint arXiv:2003.08567, 2020
1022020
A-Wristocracy: Deep Learning on Wrist-worn Sensing for Recognition of User Complex Activities
P Vepakomma, D De, SK Das, S Bhansali
IEEE Body Sensor Networks Conference, 2015
1022015
Detailed comparison of communication efficiency of split learning and federated learning
A Singh, P Vepakomma, O Gupta, R Raskar
https://arxiv.org/pdf/1909.09145.pdf, 2019
95*2019
No peek: A survey of private distributed deep learning
P Vepakomma, T Swedish, R Raskar, O Gupta, A and Dubey
arXiv preprint arXiv:1812.03288 8, 2018
532018
Fedml: A research library and benchmark for federated machine learning
C He, S Li, J So, M Zhang, H Wang, X Wang, P Vepakomma, A Singh, ...
SpicyFL, NeurIPS 2020, 2020
502020
Assessing Disease Exposure Risk With Location Histories And Protecting Privacy: A Cryptographic Approach In Response To A Global Pandemic
A Berke, M Bakker, P Vepakomma, R Raskar, K Larson, AS Pentland
44*2020
Reducing Leakage In Distributed Deep Learning For Sensitive Health Data
P Vepakomma, O Gupta, D Abhimanyu, R Raskar
ICLR AI for Social Good, 2019
442019
Privacy in Deep Learning: A Survey
F Mirshghallah, M Taram, P Vepakomma, A Singh, R Raskar, ...
362020
Supervised Dimensionality Reduction via Distance Correlation Maximization
P Vepakomma, C Tonde, A Elgammal
Electronic Journal of Statistics (Journal) 12 (1), 960-984, 2018
302018
Split Learning for collaborative deep learning in healthcare
MG Poirot, P Vepakomma, K Chang, J Kalpathy-Cramer, R Gupta, ...
292019
A Review of Homomorphic Encryption Libraries for Secure Computation
SS Sathya, P Vepakomma, R Raskar, R Ramachandra, S Bhattacharya
242018
A Fast Algorithm for Manifold Learning by Posing it as a Symmetric Diagonally Dominant Linear System
P Vepakomma, A Elgammal
Applied and Computational Harmonic Analysis, 2016
142016
NoPeek: Information leakage reduction to share activations in distributed deep learning
P Vepakomma, A Singh, O Gupta, R Raskar
IEEE ICDM-W, 2020
102020
Splitnn-driven vertical partitioning
I Ceballos, V Sharma, E Mugica, A Singh, A Roman, P Vepakomma, ...
arXiv preprint arXiv:2008.04137, 2020
82020
Prediction accuracy in a spatio-temporal prediction system
P Vepakomma, E Copp, A Reynolds
US Patent App. 14/480,523, 2015
82015
Data Markets to support AI for All: Pricing, Valuation and Governance
R Raskar, P Vepakomma, T Swedish, A Sharan
https://arxiv.org/pdf/1905.06462.pdf, 2019
62019
ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries
V Sharma, P Vepakomma, T Swedish, K Chang, J Kalpathy-Cramer, ...
https://arxiv.org/pdf/1910.02312.pdf, 2019
42019
Ppcontacttracing: A privacy-preserving contact tracing protocol for covid-19 pandemic
P Singh, A Singh, G Cojocaru, P Vepakomma, R Raskar
arXiv preprint arXiv:2008.06648, 2020
32020
Het systeem kan de bewerking nu niet uitvoeren. Probeer het later opnieuw.
Artikelen 1–20