Natalia Díaz Rodríguez

@NataliaDiazRodr

Asst Prof +. Co-founder. Deep & Reinforcement Learning, Robotics, Neural-Symbolic Systems, Explainable AI, AI 4 Social Good

Paris, France
Vrijeme pridruživanja: listopad 2008.

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  1. Prikvačeni tweet

    Our last Survey and Taxonomies on eXplainable AI and Responsible AI: et al. Enjoy! 🤖

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  2. proslijedio/la je Tweet
    6. velj

    Organizers of data science and machine learning conferences (NeurIPS, ICML, AISTATS, ICLR, UAI, ...): Allow remote paper & poster presentations at conferences - Sign the petition! via

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  3. proslijedio/la je Tweet
    4. velj

    Great work showing how intrinsically motivated goal exploration can be used to automate discovery of novel patterns in wet chemical systems !!! Congratulations and team! Intrinsically motivated algorithms open new perspectives in physics/chemistry

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    The field is already self-correcting. Good departments/labs are clearing their eyes, caring less about paper count, seeing through the noise. Don't worry so much about the ICML deadline. Slow down, relax, try to do work you're proud of, submit when it's ready.

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  5. proslijedio/la je Tweet
    1. velj

    You can now apply to be an organizer for 2020 events! Please apply using the following form: . We are very excited to receive your applications! Deadline is on Feb 14th.

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  6. proslijedio/la je Tweet
    3. velj

    It was a great opportunity to discuss with more than 20 researchers about generative models and how they can be used in Continual Learning! This was a great experience and truly enriching! Thanks to the community! 😊

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  7. proslijedio/la je Tweet
    29. sij

    ‘Mapping the future’: our recent paper, which provides insight into previously unexplained elements of dopamine-based learning in the brain, is on the front cover of ! 🎉 Read the blog:

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  8. proslijedio/la je Tweet
    27. sij

    Just returned from the 2020 winter school on fairness, accountability, and transparency in AI. It was really cool to talk about these issues within an interdisciplinary community. Here are some of my takeaways/reminders:

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  9. proslijedio/la je Tweet
    25. sij

    Join us for the 1st annual conference : AI for Humanity and Society. In Stockholm, 12 March. Registration and information

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  10. proslijedio/la je Tweet
    24. sij

    is one of the most important techniques I don't often recommend PhD Thesis' - 's is exceptional. He's a brilliant writer! Check out this taxonomy / table of contents!!! 👇👇👇

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  11. proslijedio/la je Tweet
    23. sij

    Great time with great people from a winter school 2020. , @Aggizzz95, , , , ,

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  13. proslijedio/la je Tweet
    16. sij

    Really excited about this initiative for Independent Researchers and alike, by . Want to collaborate on research with the goal to publish, contact Night AI on hello@nightai.co !

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  14. proslijedio/la je Tweet
    22. sij

    A way to mitigate gradient interference in multi-task learning, which works well in supervised and reinforcement learning. Pretty simple to set up too!

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  15. proslijedio/la je Tweet
    21. sij

    We are excited to run our first Online Meetup on "Generative Models for Continual Learning", you're all invited to join the 27th at 4pm CEST!

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  16. proslijedio/la je Tweet
    21. sij
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  18. proslijedio/la je Tweet
    16. sij

    Our review paper on Explainable Artificial Intelligence has been published in the Elsevier Information Fusion Journal! et al. Check out the 50 days open access article:

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  19. Terrific programme & speakers at NeSy 2020 (Neural-Symbolic Learning and Reasoning) Workshop (jointly with ILP, StarAI and AAIP) we are organizing in Athens, CFP:

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  20. proslijedio/la je Tweet
    14. sij

    Our review paper on Continual Learning for Robotics has finally been published in the Elsevier Information Fusion Journal! 🎉 Check out the 50 days open access article at the link below! 👇

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  21. proslijedio/la je Tweet

    Although neural networks usually require massive datasets to do impressive things, for me the highlight of is the fact that it achieved state-of-the-art using only 30K training examples. Code: Paper:

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