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- ArticleFebruary 2023
A Study on Self-Supervised Object Detection Pretraining
AbstractIn this work, we study different approaches to self-supervised pretraining of object detection models. We first design a general framework to learn a spatially consistent dense representation from an image, by randomly sampling and projecting ...
- ArticleMarch 2022
Quantifying Progression of Multiple Sclerosis via Classification of Depth Videos
- Peter Kontschieder,
- Jonas F. Dorn,
- Cecily Morrison,
- Robert Corish,
- Darko Zikic,
- Abigail Sellen,
- Marcus D’Souza,
- Christian P. Kamm,
- Jessica Burggraaff,
- Prejaas Tewarie,
- Thomas Vogel,
- Michela Azzarito,
- Ben Glocker,
- Peter Chin,
- Frank Dahlke,
- Chris Polman,
- Ludwig Kappos,
- Bernard Uitdehaag,
- Antonio Criminisi
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014Pages 429–437https://doi.org/10.1007/978-3-319-10470-6_54AbstractThis paper presents new learning-based techniques for measuring disease progression in Multiple Sclerosis (MS) patients. Our system aims to augment conventional neurological examinations by adding quantitative evidence of disease progression. An ...
- research-articleJune 2024
Revealing and protecting labels in distributed training
NIPS '21: Proceedings of the 35th International Conference on Neural Information Processing SystemsArticle No.: 133, Pages 1727–1738Distributed learning paradigms such as federated learning often involve transmission of model updates, or gradients, over a network, thereby avoiding transmission of private data. However, it is possible for sensitive information about the training data ...
- research-articleNovember 2021
TAK-ML: Applying Machine Learning at the Tactical Edge
- Peter Chin,
- Emily Do,
- Cody Doucette,
- Brandon Kalashian,
- David Last,
- Nathan Lenz,
- Edward Lu,
- Devon Minor,
- Elias Noyes,
- Colleenn Rock,
- Nathaniel Soule,
- Nicholas Walczak,
- Dave Canestrare
MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM)Pages 108–114https://doi.org/10.1109/MILCOM52596.2021.9652909The “Every Soldier is a Sensor” (ES2) concept employs warfighters' proximity to unfolding events in order to provide better situational awareness and decision-making capabilities. However, today's ES2 practices put the burden ...
- ArticleDecember 2018
Learning to repair software vulnerabilities with generative adversarial networks
- Jacob A. Harer,
- Onur Ozdemir,
- Tomo Lazovich,
- Christopher P. Reale,
- Rebecca L. Russell,
- Louis Y. Kim,
- Peter Chin
NIPS'18: Proceedings of the 32nd International Conference on Neural Information Processing SystemsPages 7944–7954Motivated by the problem of automated repair of software vulnerabilities, we propose an adversarial learning approach that maps from one discrete source domain to another target domain without requiring paired labeled examples or source and target ...
- research-articleNovember 2016
Wi(deband)-Fi: A Proposal for an Opportunistic Wideband Architecture based on Wi-Fi
MobiWac '16: Proceedings of the 14th ACM International Symposium on Mobility Management and Wireless AccessPages 115–122https://doi.org/10.1145/2989250.2989256We propose a system, Wideband-Fi, that is motivated by the information theoretic optimality of impulsive frequency shift keying (I-FSK) in wideband systems, and by the availability of orthogonal frequencies in current Wi-Fi systems. Using orthogonal ...
- ArticleDecember 2014
Computing Diffusion State Distance Using Green’s Function and Heat Kernel on Graphs
AbstractThe diffusion state distance (DSD) was introduced by Cao-Zhang-Park-Daniels-Crovella-Cowen-Hescott [PLoS ONE, 2013] to capture functional similarity in protein-protein interaction networks. They proved the convergence of DSD for non-bipartite ...