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Accept rate seems like a red herring as indicator of conference quality. At a conference I expect to appreciate O(1) papers, not O(r n). If anything, adapting accept rate to number of attendees makes sense to me to make sure everyone gets their O(1). https://twitter.com/acganesh/status/1170775288172515329 …
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Here's a clever use of machine learning in astronomy. To predict the location of an asteroid, they fit a complicated parametric model inspired by physics to a few observations of the trajectory. Spoiler: Gauss, 1809. https://math.berkeley.edu/~mgu/MA221/Ceres_Presentation.pdf …
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Statistics twitter, is my interpretation correct that Robbins 1951 discovered Stein's phenomenon on ±1 vectors 5 years before Stein discovered it for R^n? https://projecteuclid.org/download/pdf_1/euclid.bsmsp/1200500224 …
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Ali Rahimi proslijedio/la je Tweet
If you are working on empirical phenomena in deep learning, consider submitting to our ICML workshop "Identifying and Understanding Deep Learning Phenomena" (http://deep-phenomena.org/ ). The deadline is May 5, but relevant work that was already published elsewhere is still welcome!
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can we just pretend that in some parts of the world, "pleaser consider" is a totally acceptable idiom?
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my second favorite thing about this talk is that at the last minute, he scrapped the talk he was planning to give, and stayed up late late into the night to write this one, based on conversations he'd had over dinner.
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A mind-blowing talk by
@koerding on how we develop deep learning algos, and what it means to "understand" these algos (spoiler: by construction, not understandable, he says)https://www.youtube.com/watch?v=GgzRdQ5nm0U …Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
... and an even better step at getting to know its fabulous organizers,
@aleks_madry@HanieSedghi@maithra_raghu@lschmidt3@arimorcos@kenjihata and Ying Xiao.Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Pleaser consider submitting (or attending) our workshop on Identifying and Understanding Deep Learning Phenomena (http://deep-phenomena.org ). I consider the workshop a step at gaining the kind of understanding I wish we had.
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protip, maybe: if you really think your technology is so evil that it should be kept in a bottle, maybe don't build it in the first place? otherwise people might think it's just hype& vaporware.
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Ali Rahimi proslijedio/la je Tweet
I envision a field that is neither. A field mindful of its own limitations that approaches other disciplines with respect and curiosity.https://twitter.com/Aaroth/status/1044582986983759873 …
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Ali Rahimi proslijedio/la je Tweet
(Thread) Some thoughts about peer review post-NIPS:
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After squad was declared solved, squad 2 emerged. Maybe similar to ImageNet -> COCO, as
@sguada pointed out.pic.twitter.com/oiOzCJIg2W
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Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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here are a few more, to tell a more balanced story. Thanks to
@kenjihata for help. PASCAL VOC:pic.twitter.com/fHZfEmxQcf
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to clarify, I'm trying to map where DL is in Christensen's technology S-curves:pic.twitter.com/81cIUKB7Tf
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Just plotted this to check if we're plateauing in computer vision performance, and if it's time to invest in a new wave of techniques. Do you find this this convincing? (unsure what human perf is for this)pic.twitter.com/TpbL05Bnw3
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If you have to say one of these things about this paper, 1) we've overfitt to CIFAR10 validation set 2) its validation has been resilient to overfitting, pls pick 2 (the claim is even subtler). I've heard 3 times this week that ML is screwed because of this. We're not screwed.https://twitter.com/beenwrekt/status/1003641776224264192 …
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