173 citations · 286 across the 9 of their papers we have counts for
9 papers
Automatic calibration of time of flight based non-line-of-sight reconstruction
Subhash Chandra Sadhu, Abhishek Singh, Tomohiro Maeda +4
Time of flight based Non-line-of-sight (NLOS) imaging approaches require precise calibration of illumination and detector positions on the visible scene to produce reasonable resul…
Recent Advances in Imaging Around Corners
Tomohiro Maeda, Guy Satat, Tristan Swedish +2
Seeing around corners, also known as non-line-of-sight (NLOS) imaging is a computational method to resolve or recover objects hidden around corners. Recent advances in imaging arou…
ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations
Vivek Sharma, Praneeth Vepakomma, Tristan Swedish +3
Recently, there has been the development of Split Learning, a framework for distributed computation where model components are split between the client and server (Vepakomma et al.…
ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries
Vivek Sharma, Praneeth Vepakomma, Tristan Swedish +3
In this work we introduce ExpertMatcher, a method for automating deep learning model selection using autoencoders. Specifically, we are interested in performing inference on data s…
Data Markets to support AI for All: Pricing, Valuation and Governance
Ramesh Raskar, Praneeth Vepakomma, Tristan Swedish +1
We discuss a data market technique based on intrinsic (relevance and uniqueness) as well as extrinsic value (influenced by supply and demand) of data. For intrinsic value, we expla…
No Peek: A Survey of private distributed deep learning
Praneeth Vepakomma, Tristan Swedish, Ramesh Raskar +2
We survey distributed deep learning models for training or inference without accessing raw data from clients. These methods aim to protect confidential patterns in data while still…
Split learning for health: Distributed deep learning without sharing raw patient data
Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish +1
Can health entities collaboratively train deep learning models without sharing sensitive raw data? This paper proposes several configurations of a distributed deep learning method…
3D Traffic Simulation for Autonomous Vehicles in Unity and Python
Zhijing Jin, Tristan Swedish, Ramesh Raskar
Over the recent years, there has been an explosion of studies on autonomous vehicles. Many collected large amount of data from human drivers. However, compared to the tedious data…
Learning Gaze Transitions from Depth to Improve Video Saliency Estimation
G. Leifman, D. Rudoy, T. Swedish +2
In this paper we introduce a novel Depth-Aware Video Saliency approach to predict human focus of attention when viewing RGBD videos on regular 2D screens. We train a generative con…