Thank you for those golden memories! Watching the G.O.A.T playing while growing up is an honor! We can't ask more from you yet we'll miss you everyday! 😢😢Messi, we luv you 10,000! ⚽️⚽️⚽️💕💕💕
#messiretirement
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May the scientific endeavour continues.. Happy 250th Birthday, United States of America!
🤖 Made with AI
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I asked #ChatGPT o1 mini to generate a pic of a christmas tree in the middle of a crowded market place of #Darjeeling on a snowy evening. Instead it responded with a short para asking me to imagine with it! I feed in to #Gemini step by step and got this! #MerryChristmas
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How robust are equivariant convolutional representations w.r.t. deformations (incld. adversarial examples)?
One answer lies in understanding equivariant CNNs through the lens of equivariant CKNs, following a standard analysis of embedding CNNs in RKHSs. @GRaM_workshop
We generalize the notion of convolutional kernel networks (ckns) to make it equivariant to any Lie group transformations and then study it's stability bounds.
As one can embed equiv-CNNs in equiv-CKNs, such Lipschitz styled bound can be useful in understanding the robustness..
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.. of equiv-CNNs against deformations which would include adversarial examples. Some experiments with SE(2)-CKNs and SO(3)-CKNs have been provided to support the claims.
To be presented virtually at @GRaM_workshop
Paper link: openreview.net/forum?id=A9CR…
Questions? Send an email!
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