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Reiichiro Nakano 18. pro
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."
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Robert Lange 16. pro
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:
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Rachel Thomas 17. pro
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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Susan Athey 14. pro
A first for me at a conference: more invited talks by women named Susan than any other category...
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Luba Elliott 18. pro
My AI art online gallery for 2019 is finally live 🎉 🎉🎉 Check out all the art, music and design projects submitted to our Workshop😍
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Roger Melko 14. pro
Machine Learning and the Physical Sciences will live stream
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Dumitru Erhan 15. pro
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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hardmaru 18. pro
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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Reza Zadeh 17. pro
Very detailed notes of . 70 pages by David Abel.
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Yujia Li 13. pro
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...
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Dan Rosenbaum 31. pro
Videos of all the great talks from the workshop are now available on the webpage, along with the accepted papers!
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Ashley D. Edwards 16. pro
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):
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Adam Kosiorek 16. pro
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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Andrew Gordon Wilson 27. pro
Odgovor korisniku/ci @andrewgwils
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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Jeffrey Heer 16. pro
My invited talk, "Agency + Automation: Designing Artificial Intelligence into Interactive Systems" is online here:
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Andrew Gordon Wilson 16. pro
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:
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Andrew Ng 19. pro
Reflecting on . Great meeting as usual, but the AI community is maturing, which felt bittersweet. Bitter because:
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NeurIPS Banglore meetup 2. velj
“100x faster Hyperparameter Search Framework with Pyspark” by Rahul Agarwal
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NeurIPS Banglore meetup 2. velj
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Dmytro Mishkin 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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