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. 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:

  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. 14. pro 2019.

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

  5. 14. pro 2019.

    Machine Learning and the Physical Sciences will live stream

  6. 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):

  7. 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😍

  8. 14. pro 2019.

    is sadly coming to an end and I just wanted to remark on how great the venue is: Canada Place is remarkably well-located, the convention center is walkable to so much good stuff in the city. I enjoy that we get to mingle with locals that take evening strolls here.

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  9. 18. pro 2019.

    Neural networks grown and self-organized by noise Inspired by the development of the mammalian visual system, they propose an way to grow and self-organize a retinotopic pooling architecture that behaves like CNNs. Found this weird work at :

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  10. 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.

  11. 31. pro 2019.

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

  12. 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...

  13. 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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  14. 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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  15. 19. pro 2019.

    At I spoke at the Bayesian Deep Learning workshop on the central limit theory for multiple hidden layers and then on the panel. Slides and video here:

  16. 2. velj

    “100x faster Hyperparameter Search Framework with Pyspark” by Rahul Agarwal

  17. 2. velj
  18. 1. velj

    Good time to remind about this paper from the : "Avoiding a Tragedy of the Commons in the Peer Review Process"

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  19. 1. velj

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