Ludovic Denoyer

@LudovicDenoyer

Research Scientist at Facebook - FAIR Paris - previously Professor at Sorbonne Université

Paris, France
www-connex.lip6.fr/~denoyer
Vrijeme pridruživanja: travanj 2014.
15 fotografija ili videozapisa Fotografije i videozapisi

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  1. proslijedio/la je Tweet
    11. pro 2019.

    Come discuss our work on Unsupervised Object Segmentation at this morning’s poster session (number 84) ! Work done with my supervisors Thierry Artieres and .

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  2. proslijedio/la je Tweet
    27. lis 2019.
    Odgovor korisniku/ci

    In the tradition of Drew McDermott's "How Intelligent is Deep Blue?"--that I keep making my Intro students read..

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  3. proslijedio/la je Tweet
    23. lis 2019.

    Last Thursday has defended his thesis : « Unsupervised Machine Translation » Supervised by and Marc Ranzato Congratulations !!!

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  4. 23. lis 2019.

    Go !!!

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  5. proslijedio/la je Tweet
    21. ruj 2019.

    The “Research in Brief” blog post on our ACL paper on unsupervised QA is up! Work with and - check out the full paper here:

    Facebook AI is releasing code for a self-supervised technique that uses AI-generated questions to train systems, avoiding the need for labeled question answering training data.
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  6. proslijedio/la je Tweet
    20. ruj 2019.

    Facebook AI is releasing code for a self-supervised technique that uses AI-generated questions to train systems, avoiding the need for labeled question answering training data.

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  7. 13. ruj 2019.

    Can we learn persons specific language models that evolve through time ? Check our last article: "Learning Dynamic Author Representations with Temporal Language Models" with and S. Lamprier (ICDM) at

    10 proslijeđenih tweetova 14 korisnika označava da im se sviđa
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  8. proslijedio/la je Tweet
    10. ruj 2019.

    Continuation of the starting year saga advertising our recent publication achievements. (2/2) "Learning Dynamic Author Representations with Temporal Language Models" by , , Congrats to all authors!!!!

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  9. proslijedio/la je Tweet
    3. ruj 2019.

    In the second we show that adding a Product-Key Memory Layer in a transformer is as efficient as doubling the number of layers in terms of performance, and has no impact on running time. with Marc'Aurelio Ranzato (2/3)

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  10. 3. ruj 2019.

    Congrats !!! Now, next step is finishing your thesis manuscript 😉😁

    Our work on Unsupervised Object Segmentation by Redrawing is accepted at as a poster! with Thierry Artières and We discover meaningful regions by re-generating their content separately: Code: Preprint:
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  11. proslijedio/la je Tweet
    2. kol 2019.

    @pytorchLightin, the keras for researchers will be added to the official ecosystem next week.

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  12. proslijedio/la je Tweet
    31. srp 2019.

    You can actually train a model for SQuAD without training data!! 🤯 Patrick Lewis () on Unsupervised Question Answering by Cloze Translation Hall 4

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  13. proslijedio/la je Tweet
    30. srp 2019.

    Come to see Patrick’s work on QA without QA supervision. Definitely worth finishing your lunch in time!

    Hey , is it possible to train extractive QA models without supervision? Come to our oral presentation on Wednesday 1:50pm Hall 4 to find out - “Unsupervised Question Answering by Cloze translation” 🚀
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  14. proslijedio/la je Tweet
    12. srp 2019.

    Our new paper: Large Memory Layers with Product Keys We created a key-value memory layer that can increase model capacity for a negligible computational cost. A 12-layer transformer with a memory outperforms a 24-layer transformer, and is 2x faster! 1/2

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  15. proslijedio/la je Tweet
    19. lip 2019.

    First talk : Image generative modeling for design inspiration and image editing by Camille Couprie, Research Scientist

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  16. proslijedio/la je Tweet
    19. lip 2019.

    Nous sommes ravis d’accueillir la 17ème rencontre de l’association Women in Machine Learning & Data Science. Cette association favorise la participation des femmes et des minorités de genre qui pratiquent et étudient l'apprentissage machine et les data science.

    We are on!
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  17. 13. lip 2019.

    Can we train Question Answering models without a QA training set ? Congrats for this paper !

    0:38
    Question Answering models are data hungry. But what if there was no QA training data at all? Very excited to share "Unsupervised Question Answering by Cloze Translation" ACL 2019, with and !
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  18. proslijedio/la je Tweet
    6. lip 2019.

    Is CycleGAN implementing an Optimal Transport (OT) Plan between domains? New work of Emmanuel de Bézenac, Ibrahim Ayed and Patrick Gallinari from MLIA, analyzing Unsupervised Domain Translation under the framework of OT. arxiv:

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  19. 6. lip 2019.

    For those interested by this line of research, please consider other papers by who is a very great PhD student: * - Multi-view Generative Adversarial Networks * - Multi-View Data Generation Without View Supervision

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  20. 5. lip 2019.

    Can we learn to detect objects without any supervision? Yes, if we assume that an object is a part of an image that can be redrawn while keeping the image realistic. With and Thierry Artieres -

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