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Randal Burns

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Prospective Learning: Back to the Future


Jan 19, 2022
Joshua T. Vogelstein, Timothy Verstynen, Konrad P. Kording, Leyla Isik, John W. Krakauer, Ralph Etienne-Cummings, Elizabeth L. Ogburn, Carey E. Priebe, Randal Burns, Kwame Kutten, James J. Knierim, James B. Potash, Thomas Hartung, Lena Smirnova, Paul Worley, Alena Savonenko, Ian Phillips, Michael I. Miller, Rene Vidal, Jeremias Sulam, Adam Charles, Noah J. Cowan, Maxim Bichuch, Archana Venkataraman, Chen Li, Nitish Thakor, Justus M Kebschull, Marilyn Albert, Jinchong Xu, Marshall Hussain Shuler, Brian Caffo, Tilak Ratnanather, Ali Geisa, Seung-Eon Roh, Eva Yezerets, Meghana Madhyastha, Javier J. How, Tyler M. Tomita, Jayanta Dey, Ningyuan, Huang, Jong M. Shin, Kaleab Alemayehu Kinfu, Pratik Chaudhari, Ben Baker, Anna Schapiro, Dinesh Jayaraman, Eric Eaton, Michael Platt, Lyle Ungar, Leila Wehbe, Adam Kepecs, Amy Christensen, Onyema Osuagwu, Bing Brunton, Brett Mensh, Alysson R. Muotri, Gabriel Silva, Francesca Puppo, Florian Engert, Elizabeth Hillman, Julia Brown, Chris White, Weiwei Yang


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PACSET (Packed Serialized Trees): Reducing Inference Latency for Tree Ensemble Deployment


Nov 10, 2020
Meghana Madhyastha, Kunal Lillaney, James Browne, Joshua Vogelstein, Randal Burns

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Geodesic Learning via Unsupervised Decision Forests


Jul 05, 2019
Meghana Madhyastha, Percy Li, James Browne, Veronika Strnadova-Neeley, Carey E. Priebe, Randal Burns, Joshua T. Vogelstein

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Random Projection Forests


Oct 10, 2018
Tyler M. Tomita, James Browne, Cencheng Shen, Jesse L. Patsolic, Jason Yim, Carey E. Priebe, Randal Burns, Mauro Maggioni, Joshua T. Vogelstein

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* 46 pages; submitted to Journal of Machine Learning Research for review on 09/26/2018 
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Linear Optimal Low Rank Projection for High-Dimensional Multi-Class Data


Feb 27, 2018
Joshua T. Vogelstein, Minh Tang, Eric Bridgeford, Da Zheng, Randal Burns, Mauro Maggioni

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* 6 figures 
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VESICLE: Volumetric Evaluation of Synaptic Interfaces using Computer vision at Large Scale


Sep 07, 2015
William Gray Roncal, Michael Pekala, Verena Kaynig-Fittkau, Dean M. Kleissas, Joshua T. Vogelstein, Hanspeter Pfister, Randal Burns, R. Jacob Vogelstein, Mark A. Chevillet, Gregory D. Hager

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* Proceedings of the British Machine Vision Conference (BMVC), pages 81.1-81.13. BMVA Press, September 2015 
 * v4: added clarifying figures and updates for readability. v3: fixed metadata. 11 pp v2: Added CNN classifier, significant changes to improve performance and generalization 
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An Automated Images-to-Graphs Framework for High Resolution Connectomics


Apr 30, 2015
William Gray Roncal, Dean M. Kleissas, Joshua T. Vogelstein, Priya Manavalan, Kunal Lillaney, Michael Pekala, Randal Burns, R. Jacob Vogelstein, Carey E. Priebe, Mark A. Chevillet, Gregory D. Hager

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* 13 pages, first two authors contributed equally V2: Added additional experiments and clarifications; added information on infrastructure and pipeline environment 
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Automatic Annotation of Axoplasmic Reticula in Pursuit of Connectomes using High-Resolution Neural EM Data


Apr 16, 2014
Ayushi Sinha, William Gray Roncal, Narayanan Kasthuri, Jeff W. Lichtman, Randal Burns, Michael Kazhdan

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* 2 pages, 1 figure; The 3rd Annual Hopkins Imaging Conference, The Johns Hopkins University, Baltimore, MD 
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Automatic Annotation of Axoplasmic Reticula in Pursuit of Connectomes


Apr 16, 2014
Ayushi Sinha, William Gray Roncal, Narayanan Kasthuri, Ming Chuang, Priya Manavalan, Dean M. Kleissas, Joshua T. Vogelstein, R. Jacob Vogelstein, Randal Burns, Jeff W. Lichtman, Michael Kazhdan

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* 2 pages, 1 figure 
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Model-Based Event Detection in Wireless Sensor Networks


Jan 25, 2009
Jayant Gupchup, Andreas Terzis, Randal Burns, Alex Szalay

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* Workshop for Data Sharing and Interoperability on the World Wide Web (DSI 2007). April 2007, In Proceedings 
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