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sebastien_wood proslijedio/la je Tweet
I made a goose that destroys your computer Download it free here: http://samperson.itch.io/desktop-goose pic.twitter.com/cCGdoOYW1Y
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sebastien_wood proslijedio/la je Tweet
There's a *lot* of people that are under the impression that publishers pay for stuff that's actually done by volunteers or open source software. As a result, many people over-estimate the amount of costs that are reasonable to expect. https://twitter.com/jeremyphoward/status/1219365213201264640 …pic.twitter.com/RxcVLowhf9
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sebastien_wood proslijedio/la je Tweet
For some reason, I haven't been invited to Davos this year. Was it something I said?
pic.twitter.com/6Qmrup7HrB
NowThis
Davos Panelist Calls Out Davos For Not Addressing the Obvious‘It feels like I’m at a firefighters conference and no one’s allowed to speak about water.’ — This historian wasn’t afraid to confront the billionaires at Davos about their greedHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
sebastien_wood proslijedio/la je TweetHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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sebastien_wood proslijedio/la je Tweet
It’s been 20 years since I submitted my first paper with Nhat Nguyen and the late great Gene Golub on multi-frame super-res (SR). Here’s a thread, a personal story of SR as I’ve experienced it. It won’t be exhaustive or fully historical. Apologies to colleagues for any omissionspic.twitter.com/GBn7kHNfqC
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The use case is when there are possible orthogonalities in your experiment/idea and the state of the art shiny bag of trick thingy. E.g., is a clear signal of value in your idea more important than trying to look cool by getting top 1 ?
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When preparing for an experiment, do you believe it is more useful to compare with the strongest baseline available (as to get into a virtual competition), or is it better to compare with a standard baseline with marginally lower results ?
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sebastien_wood proslijedio/la je Tweet
Swap x and y without using a third variable. x = x^y y = y^x x = x^y where ^ is XOR.
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sebastien_wood proslijedio/la je Tweet
Some work-in-progress illustrations to gently introduce some concepts for 2-sample t-tests & how to think about p-values. Meant as teaching aids, not as a comprehensive standalone lesson. Caveats & assumptions abound.
pic.twitter.com/NazUIXrr8z
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sebastien_wood proslijedio/la je TweetHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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sebastien_wood proslijedio/la je Tweet
No. No it reaally can’t. (Thread)https://twitter.com/DiscussingFilm/status/1215007789547999235 …
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sebastien_wood proslijedio/la je Tweet
Patterns produced by neural network with random weights can give us some insights about the system’s inductive biaseshttps://twitter.com/etiennejcb/status/1213781328426127362 …
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sebastien_wood proslijedio/la je Tweet
Lloyd’s algorithm is the continuous counterpart of k-means. Optimizes the optimal quantization energy, which is an optimal transport distance to free Dirac masses. https://en.wikipedia.org/wiki/Lloyd%27s_algorithm … https://en.wikipedia.org/wiki/Vector_quantization …pic.twitter.com/kDENINfetH
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sebastien_wood proslijedio/la je Tweet
Public transit should be free. The fact that it's not is a policy choice. We choose to prioritize cars & spend money cracking down on fare evasion. This worsens carbon emissions & hurts the poor. Public transit should be free.
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(compared to a binary network with reliable memory) Details are in the paper, which we'll present at AICAS 2020
Interested ? have any question ? let's chat !
otherwise, merry christmas !
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Thus we propose the Layerwise Noise Maximisation algorithm to efficiently find the "good amount" of randomness during a gradient descent based training. We show networks have the capability to retain an interesting accuracy with a third of the original memory energy consumption.
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Or, you could try to optimize the network to work with these random parameters and even better: try to maximize the amount of randomness. Based on recent works such as "Are all layers created equal" (Zhang, Bengio et al.) we propose to optimize the randomness per-layer.
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You could use some safety mechanisms to ensure the read parameters are "the good ones" (e.g. error correction code). But let's agree it's neither fun nor free (require additional hardware
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It's no free lunch though ! The memory read error rate, meaning 0's read as 1's and vice-versa, will undergo an exponential increase. Your network will thus have to make with randomized parameters.
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To squeeze even further the consumption you're then bound to tweak your architecture ... but are you ? If you take a look at the hardware part, the consumption grows quadratically with the supply voltage. You could try to play with it
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