2021.09.28 20:44 DanielPokeFusions First Attempt at a Hexafusion
2021.09.28 20:44 AbbreviationsSpare59 Kencarson x Liluzi🔥🔥
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2021.09.28 20:44 deannag4 [Offer] Handmade Autumn Cards [2 UK, 2 WW]
As an early birthday celebration to myself and because I have some fun autumn themed crafts I want to use - 4 handmade cards are on offer!
Please add a comment with your favourite thing about autumn (or Fall for those in the US!) and I will try to incorporate it!
submitted by deannag4 to RandomActsofCards [link] [comments]
2021.09.28 20:44 sweetmanaphy Loving the fall weather
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2021.09.28 20:44 MyNewAlt5836484 Triple Styros baby!
2021.09.28 20:44 MsFloppyMew I sell handmade jewelry and clay made figurines. If you want to check it out
2021.09.28 20:44 SpanishMeme Cosas que pueden pasarte un día cualqueira en Rusia
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2021.09.28 20:44 TuaTurnsdaballova Jakobi-Wan Kenobi
Jakobi Meyers was already the presumptive WR1 on the Pats, but only by default because there are so many mouths to feed between White, Agholor, Bourne, Henry, and Jonnu. For fantasy purposes, he was considered a mid-WR3 with no upside (he doesn’t get TDs).
However, everything was about to change in Week 3:
Jakobi had 15 targets and 95 yards. Was this his breakout week? Has a new era of Patriots football dominance begun with the Mac-to-Jakobi connection?
James White, who was getting 6-7 targets a game, is suddenly out indefinitely. The third down RB role is a mess with Bolden/TayloStephenson all vying for the job, and none of them look particularly good. Well, through that chaos, it looks like someone just captured a huge chunk of White’s target share...
From the top rope!
Enter Jakobi Meyers. A once middling WR3 is suddenly on the cusp of becoming a weekly WR2 (with upside!).
While Jakobi hasn’t been able to haul in a TD yet, we know he’s capable of throwing TDs (he was a QB in high school and college), and we know Belichick trusts him enough to call those trick plays every now and then. But more importantly, Mac Jones is quickly developing chemistry with our Jedi Master, and a rookie QB’s safety blanket can become invaluable.
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2021.09.28 20:44 SpanishMeme Quizá no todos querían volver
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2021.09.28 20:44 asiao literally
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2021.09.28 20:44 Bacnwarrior Co-op wins fut
2021.09.28 20:44 pratik_gehlot Anyone here using Grammarly? for helping with SOP and other parts of the application?
2021.09.28 20:44 DN-838 This server really can become a [BigShot]
2021.09.28 20:44 Wise-Affect8006 Hello everyone, I am from Hong Kong and I am now in the United States. I have been here for seven years, but because my English is not very good, it is not easy for me to get to know some American friends. If someone can stay together, we can build a deeper friendship.
I like to explore the unknown world, learn different new things, travel, golf, cooking and swimming. I am currently learning to paint. I love nature and appreciate beauty. Natural scenery.
I am a lovely girl who likes romanticism. If you want, you'd better come from America, because I come from Hong Kong and live in New York.
Send me a message so we can get to know each other, become friends or travel together.
submitted by Wise-Affect8006 to MakeNewFriendsHere [link] [comments]
2021.09.28 20:44 PurpleFoldingChairFC SELLING Cirez D Adam Beyer
2021.09.28 20:44 MiniatureBigMac Two things the simulation poorly portrays. Bugs, and running out of windshield washer fluid. Honestly, I'm thankful. Is there anything you think they misrepresented?
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2021.09.28 20:44 forestguhmp Philosophy: Baby walruses want wat do -><< my long and short memories
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2021.09.28 20:44 honeyandoatmeal Correspondence of Representations in Neural Networks
I'm reading papers involving understanding intermediate representations which are learned in neural networks. The purpose of these methods is to be able to compare, say, two runs of the same model with different initializations, or two different models. Here are two particular papers:
I'm hoping to see if anyone can confirm if my understanding is correct. The problem with directly comparing the weights of two models with different initializations is that the correspondence of the weights of these two models is not necessarily the same; that is, the features that are learned at a particular layer in the network need not "line up". From the first paper, Section 3: "Due to symmetries in the architecture and weight initialization procedures, for any given parameter vector that is found, one could create many equivalent solutions simply by permuting the unit orders within a layer (and permuting the outgoing weights accordingly)."
The way that I make sense of this is that in convolutional layers, a single output channel is computed by taking the convolution of all the input channels and the corresponding kernel (plus bias). Thus, the sum operation over all channels and kernel dimensions essentially mixes up all the information, so that any permutation of those channels would yield the same output channel. Is this correct?
If this is the case, I'm confused about how the second paper works. In there proposed similarity metric, they essentially look at the activation maps that result from N different example inputs, and create an NxN (cosine) similarity matrix which represents how similar each example's corresponding activation map is to all the other example's activation maps. From their paper, Section 3: "Our key insight is that instead of comparing multivariate features of an example in the two representations (e.g. via regression), one can first measure the similarity between every pair of examples in each representation separately, and then compare the similarity structures."
Why is it that these similarity structures can be directly compared when the weights of the network cannot, as explained in the first paper? Since the similarity structures are computed from the feature maps, does this imply that feature maps share correspondence across different networks? Why can't two networks learn a different ordering of feature maps for a particular layer? Thanks in advance for any insights.
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2021.09.28 20:44 joeyspecialtwopizzas Yikes 🥴
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2021.09.28 20:44 SilverStriker96 susie kaard
She now speaks shakespearian
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2021.09.28 20:44 Mockbubbles2628 Bread and tomato soup
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2021.09.28 20:44 arsenicCatnip Ontario doctor accused of spreading COVID-19 misinformation barred from providing vaccine, mask exemptions
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2021.09.28 20:44 wondergom83 Insane home grown indoor. @TheOfficialGreenZone. DM me for contact details.👊🏻🌿
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2021.09.28 20:44 Over-Tomatillo9822 Tendie Bets! Crypto Bets
2021.09.28 20:44 w__tommo Keeping a watchful eye…
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