Ramesh Raskar

DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks

Dec 20, 2020
Abhishek Singh, Ayush Chopra, Vivek Sharma, Ethan Garza, Emily Zhang, Praneeth Vepakomma, Ramesh Raskar

* Extended version of NeurIPS PPML 2020 workshop paper

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Proximity Sensing for Contact Tracing

Sep 04, 2020
Sheshank Shankar, Ayush Chopra, Rishank Kanaparti, Myungsun Kang, Abhishek Singh, Ramesh Raskar

* The Pathcheck team's method for TC4TL challenge

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NoPeek: Information leakage reduction to share activations in distributed deep learning

Aug 20, 2020
Praneeth Vepakomma, Abhishek Singh, Otkrist Gupta, Ramesh Raskar

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SplitNN-driven Vertical Partitioning

Aug 07, 2020
Iker Ceballos, Vivek Sharma, Eduardo Mugica, Abhishek Singh, Alberto Roman, Praneeth Vepakomma, Ramesh Raskar

* First version, please provide feedback

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FedML: A Research Library and Benchmark for Federated Machine Learning

Jul 27, 2020
Chaoyang He, Songze Li, Jinhyun So, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram, Salman Avestimehr

* We maintain the source code, documents, and user community at https://fedml.ai

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Splintering with distributions: A stochastic decoy scheme for private computation

Jul 07, 2020
Praneeth Vepakomma, Julia Balla, Ramesh Raskar

* 28 pages, 6 figures

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Automatic Differentiation for All Photons Imaging to See Inside Volumetric Scattering Media

Jun 02, 2020
Tomohiro Maeda, Ankit Ranjan, Ramesh Raskar

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Privacy in Deep Learning: A Survey

May 09, 2020
Fatemehsadat Mireshghallah, Mohammadkazem Taram, Praneeth Vepakomma, Abhishek Singh, Ramesh Raskar, Hadi Esmaeilzadeh

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Privacy Guidelines for Contact Tracing Applications

Apr 28, 2020
Manish Shukla, Rajan M A, Sachin Lodha, Gautam Shroff, Ramesh Raskar

* 10 pages, 0 images

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Split Learning for collaborative deep learning in healthcare

Dec 27, 2019
Maarten G. Poirot, Praneeth Vepakomma, Ken Chang, Jayashree Kalpathy-Cramer, Rajiv Gupta, Ramesh Raskar

* Workshop paper: 8 pages, 2 figures, 1 table

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Advances and Open Problems in Federated Learning

Dec 10, 2019
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao

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Recent Advances in Imaging Around Corners

Oct 12, 2019
Tomohiro Maeda, Guy Satat, Tristan Swedish, Lagnojita Sinha, Ramesh Raskar

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ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations

Oct 09, 2019
Vivek Sharma, Praneeth Vepakomma, Tristan Swedish, Ken Chang, Jayashree Kalpathy-Cramer, Ramesh Raskar

* In NeurIPS Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy, 2019

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ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries

Oct 05, 2019
Vivek Sharma, Praneeth Vepakomma, Tristan Swedish, Ken Chang, Jayashree Kalpathy-Cramer, Ramesh Raskar

* In NeurIPS Workshop on Machine learning for the Developing World (ML4D)

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Maximal adversarial perturbations for obfuscation: Hiding certain attributes while preserving rest

Sep 27, 2019
Indu Ilanchezian, Praneeth Vepakomma, Abhishek Singh, Otkrist Gupta, G. N. Srinivasa Prasanna, Ramesh Raskar

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Detailed comparison of communication efficiency of split learning and federated learning

Sep 18, 2019
Abhishek Singh, Praneeth Vepakomma, Otkrist Gupta, Ramesh Raskar

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Data Markets to support AI for All: Pricing, Valuation and Governance

May 14, 2019
Ramesh Raskar, Praneeth Vepakomma, Tristan Swedish, Aalekh Sharan

* 7 pages, 2 figures

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Light-Field for RF

Jan 13, 2019
Manikanta Kotaru, Guy Satat, Ramesh Raskar, Sachin Katti

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No Peek: A Survey of private distributed deep learning

Dec 08, 2018
Praneeth Vepakomma, Tristan Swedish, Ramesh Raskar, Otkrist Gupta, Abhimanyu Dubey

* 21 pages

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Split learning for health: Distributed deep learning without sharing raw patient data

Dec 03, 2018
Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, Ramesh Raskar

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Addressing the Invisible: Street Address Generation for Developing Countries with Deep Learning

Nov 10, 2018
Ilke Demir, Ramesh Raskar

* Presented at NIPS 2018 Workshop on Machine Learning for the Developing World

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* Camera-ready, accepted to NIPS 2018, v2 has minor typo updates and small changes in text

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Pairwise Confusion for Fine-Grained Visual Classification

Jul 25, 2018
Abhimanyu Dubey, Otkrist Gupta, Pei Guo, Ramesh Raskar, Ryan Farrell, Nikhil Naik

* Camera-Ready version for ECCV 2018

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DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images

May 17, 2018
Ilke Demir, Krzysztof Koperski, David Lindenbaum, Guan Pang, Jing Huang, Saikat Basu, Forest Hughes, Devis Tuia, Ramesh Raskar

* Dataset description for DeepGlobe 2018 Challenge at CVPR 2018

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Ramesh Raskar

DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks

Proximity Sensing for Contact Tracing

NoPeek: Information leakage reduction to share activations in distributed deep learning

SplitNN-driven Vertical Partitioning

FedML: A Research Library and Benchmark for Federated Machine Learning

Splintering with distributions: A stochastic decoy scheme for private computation

Automatic Differentiation for All Photons Imaging to See Inside Volumetric Scattering Media

Privacy in Deep Learning: A Survey

Privacy Guidelines for Contact Tracing Applications

Split Learning for collaborative deep learning in healthcare

Advances and Open Problems in Federated Learning

Recent Advances in Imaging Around Corners

ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations

ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries

Maximal adversarial perturbations for obfuscation: Hiding certain attributes while preserving rest

Detailed comparison of communication efficiency of split learning and federated learning

Data Markets to support AI for All: Pricing, Valuation and Governance

Light-Field for RF

No Peek: A Survey of private distributed deep learning

Split learning for health: Distributed deep learning without sharing raw patient data

Addressing the Invisible: Street Address Generation for Developing Countries with Deep Learning

3D Traffic Simulation for Autonomous Vehicles in Unity and Python

Flash Photography for Data-Driven Hidden Scene Recovery

Distributed learning of deep neural network over multiple agents

Maximum-Entropy Fine-Grained Classification

Pairwise Confusion for Fine-Grained Visual Classification

DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images