Tag Archives: #NeuralNetworks

NTR Webinar: Deep Ensembles Perspective, Part 2

NTR organizes and hosts scientific webinars on neural networks and invites speakers from all over the world to present their recent work at the webinars.

On May 18 Dmitry Vetrov, National Research University Higher School of Economics, Moscow, Russia, led a technical Zoom webinar on Deep Ensembles Perspective. It was the continuation of the first webinar that happened a month ago and brought many attendees: Surprising Properties of Loss Function in Deep Learning, Part 1. 

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NTR Webinar: Surprising Properties of Loss Function in Deep Learning

NTR organizes and hosts scientific webinars on neural networks and invites speakers from all over the world to present their recent work at the webinars.

On April 13 Dmitry Vetrov, National Research University Higher School of Economics, Moscow, Russia, led a technical Zoom webinar on Surprising Properties of Loss Function in Deep Learning. 

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NTR Webinar: Ordinary Differential Equations in Nonordinary Neural Networks

NTR organizes and hosts scientific webinars on neural networks and invites speakers from all over the world to present their recent work at the webinars.

On April 6 Talgat Daulbaev, Skoltech and Alexandr Katrutsa, Skoltech, Moscow, Russia, led a technical Zoom webinar on Ordinary Differential Equations in Nonordinary Neural Networks.

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NTR webinar: Source code author identification using neural networks

NTR organizes and hosts scientific webinars on neural networks and invites speakers from all over the world to present their recent work at the webinars.

On September 22 Anna Kurtukova, Junior Data Scientist at NTR, held a technical Zoom webinar on “Source code author identification using neural networks.”

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Info From an Indoor Drone Navigation Developer

As you may already know, one of the projects that our developers worked on (the expert AI team at NTR Lab) was a project for drones that can work indoors. NTR Lab created unique, embedded software for UAV autonomous indoor navigation.

The original object of the SLAM method was quite narrow. But more and more, as this technology becomes popular and more applicable on practice, we are moving swiftly towards a future we watched in movies.

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