Yann LeCun
Yann LeCun is an influencer

New York, New York, United States Contact Info
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Projects

  • LAGR

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    LAGR was a DARPA competition in autonomous vision-based off-road robot navigation. Nine labs competed in many races through complex off-road terrain over the course of 3 years. In the first year, our team achieved the competition's 3-year goal of improving the baseline run-time by a factor of 2, forcing the government to up the goal to a factor of 3.

    Using visual input for navigation is essential in unstructured outdoor environments, which can cause simpler sensors such as GPS…

    LAGR was a DARPA competition in autonomous vision-based off-road robot navigation. Nine labs competed in many races through complex off-road terrain over the course of 3 years. In the first year, our team achieved the competition's 3-year goal of improving the baseline run-time by a factor of 2, forcing the government to up the goal to a factor of 3.

    Using visual input for navigation is essential in unstructured outdoor environments, which can cause simpler sensors such as GPS, odometry, and laser scanners to drift severely or become unusably noisy. As an alternative to such sensors, I contributed a novel, fast form of visual odometry. Our efficient “hybrid visual odometry” method used 5% of the CPU time, in contrast to full VO methods which require an entire CPU. Despite this, it maintained comparable accuracy to full VO over diverse terrain types. This makes it particularly suitable for consumer robots, extraterrestrial rovers, and other platforms with limited computing power.

    This contribution was described in the ICRA 2009 paper Efficient Off-Road Localization Using Visually Corrected Odometry, and contributed to our JFR 2009 paper, A Multi-Range Architecture for Collision-Free Off-Road Robot Navigation.

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  • DjVu

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    DjVu (pronounced "déjà vu") is a digital document format with advanced compression technology.

    I initiated and directed the project.

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  • Check Recognition with Deep Learning

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    The first practical system for reading the amount on bank checks.
    It was deployed by NCR first in ATM machines in 1995, then in high-speed check reading machines in 1996. At some point in the late 90's these systems read an estimated 10 to 20% of all the checks in the US.

    It used a convolutional neural net, combined with a so-called graph-transformer network, a form of non conditional random field in which the solution space can be represented by a treillis.

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