Tag Archives: #Neural Networks

NTR Webinar: Measuring Compositional Generalization: A Comprehensive Method on Realistic Data

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 June 29 Dmitry Tsarkov, Google, Zurich, Switzerland led a technical Zoom webinar on Measuring Compositional Generalization: A Comprehensive Method on Realistic Data. 

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NTR Webinar: Sleep, Memory and Artificial Intelligence

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 June 22 Maksim  Bazhenov, University of California, San Diego, USA, led a technical Zoom webinar on Sleep, Memory and Artificial Intelligence. 

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NTR Webinar: Tsallis-INF for Decoupled Exploration and Exploitation in Multi-armed Bandits

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 June 15 Yevgeny Seldin, University of Copenhagen, Copenhagen, Denmark, led a technical Zoom webinar on Tsallis-INF for Decoupled Exploration and Exploitation in Multi-armed Bandits.

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NTR Webinar: Algorithm Selection: How to Get the Most Out of Your Algorithm Portfolio

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 June 8 Damir Pulatov, University of Wyoming, Laramie, WY, USA, led a technical Zoom webinar on How to Get the Most Out of Your Algorithm Portfolio

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NTR Webinar: Modern Hopfield Networks in AI and Neurobiology

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 June 1 Dmitry Krotov, MIT-IBM Watson AI Lab, IBM Research, Cambridge, MA, USA, led a technical Zoom webinar on Modern Hopfield Networks in AI and Neurobiology. 

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NTR Webinar: Unsupervised Quality Estimation for Neural Machine Translation

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 25 Marina Fomicheva, University of Sheffield, Sheffield, UK, led a technical Zoom webinar on Unsupervised Quality Estimation for Neural Machine Translation

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NTR Webinar: Excess Risk Bounds Via Stability Generalization

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 11 Yegor Klochkov, University of Cambridge, Cambridge, United Kingdom, led a technical Zoom webinar on Excess Risk Bounds Via Stability Generalization.

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NTR Webinar: Learning and Decoding with Neural Autoregressive Language Models

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 4 Ilia Kulikov, Computational Intelligence, Learning, Vision, and Robotics Lab, New York University, New York, USA, led a technical Zoom webinar on Learning and Decoding with Neural Autoregressive Language Models. 

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NTR Webinar: Learning Efficient Representations for Keyword Spotting with Triplet Loss

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 March 9 Roman Vygon, NTR Labs, led a technical Zoom webinar on Learning Efficient Representations for Keyword Spotting with Triplet Loss. 

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NTR webinar: End-to-end Spoken Language Understanding model using cross-modal Teacher-Student 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 March 2 Pavel Denisov, Institute for Natural Language Processing, University of Stuttgart, Germany, led a technical Zoom webinar on End-to-end Spoken Language Understanding model using cross-modal Teacher-Student learning.

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