Rezultati pretraživanja
  1. 17. pro 2019.

    At , talking to a guy from DeepMind about how I can't reproduce their results because it's so resource intensive. "It's not too bad actually, we only use 16 GPUs, it's not like we use a cluster of TPUs or anything."

  2. 28. sij

    If you weren't able to make it to , head over to our blog for a recap of our favorite self-driving-related papers from the conference.

  3. 17. pro 2019.

    One theme I noticed at was the need for the ML community to move beyond our narrow focus on prediction. I heard this in 3 very different talks. [Thread]

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  4. 15. pro 2019.

    What a week 🧠🤓💻! I loved meeting so many of you at - the ML community is truly wonderful. Checkout all my collected visual notes ✍️ & feel free to share:

  5. 31. pro 2019.

    Videos of all the great talks from the workshop are now available on the webpage, along with the accepted papers!

  6. 14. pro 2019.

    Machine Learning and the Physical Sciences will live stream

  7. 16. pro 2019.

    My invited talk, "Agency + Automation: Designing Artificial Intelligence into Interactive Systems" is online here:

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  8. 14. pro 2019.

    A first for me at a conference: more invited talks by women named Susan than any other category...

  9. 13. pro 2019.

    Graph representation learning is the most popular workshop of the day at . Amazing how far the field has advanced. I did not imagine so many people would get into this when I started working on graph neural nets back in 2015 during an internship. Time flies...

  10. 16. pro 2019.

    This paper had the biggest subjective "wow" factor for me at : "Deep Set Prediction Networks" by Yan Zhang, Jonathon Hare, Adam Prügel-Bennett . The idea is very simple, and yet I find it mind-blowing.

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  11. 18. pro 2019.

    My AI art online gallery for 2019 is finally live 🎉 🎉🎉 Check out all the art, music and design projects submitted to our Workshop😍

  12. 15. pro 2019.

    I had a great time at the Bayesian Deep Learning Workshop! My talk on "Using Loss Surface Geometry for Practical Bayesian Deep Learning" starts at 6m40s:

  13. 26. pro 2019.

    And we should not back away from challenges. I present some of these views in my talk at the Bayesian Deep Learning Workshop, starting at 6m40s: 18/18

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  14. 17. pro 2019.

    Want to catch up on some latest Deep RL work? Here is a zip file with all the camera-ready papers from the Deep RL Workshop!

  15. 16. pro 2019.

    I had a great time presenting my work on imitation from observation at WIML! Here are links to my slides and video :) Slides: Video (starts around 8 mins):

  16. 15. pro 2019.

    Bayesian deep learning workshop talks from NeuRIPS 2019 are already available online:

  17. 19. pro 2019.

    1/ Neural networks are Gaussian Processes --- the Poster Edition from last week. In case you missed it, here’s a twitter version of the poster presentation, following the format of ; and here’s the previous tweet thread

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  18. 15. pro 2019.

    [Impressive NeurIPS papers #2] "CNN^{2}: Viewpoint Generalization via a Binocular Vision," a brain-inspired and simple way to improve the generalizability of CNNs. Slides:

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