Robert E. Beaudoin
Life Technologies
4 Papers
1 Citations
Robert E. Beaudoin is an academic researcher from Life Technologies. The author has contributed to research in topics: Deep sequencing & Genome. The author has an hindex of 1, co-authored 2 publications.
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Papers
Sequence and structural variation in a human genome uncovered by short-read, massively parallel ligation sequencing using two-base encoding
Kevin McKernan,Heather E. Peckham,Gina Costa,Stephen F. McLaughlin,Yutao Fu,Eric F. Tsung,Christopher Clouser,Cisyla Duncan,Jeffrey K. Ichikawa,Clarence Lee,Zheng Zhang,Swati Ranade,Eileen T. Dimalanta,Fiona Hyland,Tanya Sokolsky,Lei Zhang,Andrew Sheridan,Haoning Fu,Cynthia L. Hendrickson,Bin Li,Lev Kotler,Jeremy R. Stuart,Joel A. Malek,Jonathan M. Manning,Alena A. Antipova,Damon S. Perez,Michael P. Moore,Kathleen C. Hayashibara,Michael R. Lyons,Robert E. Beaudoin,Brittany E. Coleman,Michael W. Laptewicz,Adam Sannicandro,Michael D. Rhodes,Rajesh Gottimukkala,Shan Yang,Vineet Bafna,Ali Bashir,Andrew MacBride,Can Alkan,Jeffrey M. Kidd,Evan E. Eichler,Martin G. Reese,Francisco M. De La Vega,Alan Blanchard +44 more
TL;DR: Dozens of mutations previously described in OMIM and hundreds of nonsynonymous single-nucleotide and structural variants in genes previously implicated in disease are identified in this individual.
Driving in Real Life with Inverse Reinforcement Learning
Tung Phan-Minh,Forbes E. Howington,Ting-Sheng Chu,Sang Uk Lee,Momchil Tomov,Nanxiang Li,Caglayan Dicle,Samuel Findler,Francisco Suárez-Ruiz,Robert E. Beaudoin,Bo Yang,Sammy Omari,Eric M. Wolff +12 more
TL;DR: The first learning-based planner to drive a car in dense, urban traffic using Inverse Reinforcement Learning (IRL) is introduced, with a simple design due to only learning the trajectory scoring function, relatively interpretable features, and strong real-world performance.
18
Patent
Systems and methods for identifying microparticles
Chengyong Yang,Robert E. Beaudoin,Curtis Gehman,Andrew Sheridan +3 more
- 09 Sep 2010
TL;DR: In this paper, the authors present a system for identifying microparticles or features arranged in high density arrays, such as sequencing beads or features having densities of about 39 x 10 6 particles/cm 2 or more.
1
DriveIRL: Drive in Real Life with Inverse Reinforcement Learning
Tung Phan-Minh,Forbes E. Howington,Ting-Sheng Chu,Momchil Tomov,Robert E. Beaudoin,Sang Uk Lee,Caglayan Dicle,Samuel Findler,Francisco Suárez-Ruiz,Sammy Omari,Eric M. Wolff +10 more
- 29 May 2023
TL;DR: In this article , the authors introduce the first published planner to drive a car in dense, urban traffic using Inverse Reinforcement Learning (IRL), which generates a diverse set of trajectory proposals and scores them with a learned model.