ماشین مش مندلی @manmaycry

Joined January 2014

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バズ山桜 @antikktnano

4 weeks ago

arxiv.org/abs/2605.06394

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ماشین مش مندلی @manmaycry

a month ago

@_paricaw_ فکر کنم داشته باشم یعنی تایید شده است و مقاله دارم توش شما اگه دانشجو باشید با ایمیل دانشگاهی میتونید تاییدیه رو بگیرید

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ماشین مش مندلی @manmaycry

2 months ago

@_paricaw_ من

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Your fav narcissist✨ @RavenaWatson

4 months ago

🇮🇷🇮🇷🇮🇷🇮🇷🇮🇷🇮🇷🇮🇷🇮🇷🇮🇷🇮🇷🇮🇷🇮🇷🇮🇷

Armando @armandocarroz

6 months ago

🇻🇪🇻🇪🇻🇪🇻🇪🇻🇪🇻🇪🇻🇪🇻🇪🇻🇪🇻🇪🇻🇪🇻🇪🇻🇪

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ماشین مش مندلی @manmaycry

6 months ago

@Neewwwiiiiddddd @aaliKazemii مصاحبه تو کدوم کشور؟

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ماشین مش مندلی @manmaycry

6 months ago

@OctopusUserName رشتت چیه

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ماشین مش مندلی @manmaycry

6 months ago

@db_alk حالا رنج حقوقش چقدره ؟

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Math Cafe @Riazi_Cafe_en

7 months ago

UC Davis "Measure Theory" by John K. Hunter PDF: math.ucdavis.edu/~hunter/measur…

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ماشین مش مندلی @manmaycry

7 months ago

@DanielNoroozi39 چجوری باید اینا رو گرفت

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Marc Lelarge 🌻 @marc_lelarge

7 months ago

Ready for tomorrow’s lecture: 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗼𝗻 𝗚𝗣𝗨𝘀

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ماشین مش مندلی @manmaycry

7 months ago

@codewithimanshu @f14bertolotti Pay attention to the pre-print paper that decouples the receptive field from the depth of the model arxiv.org/abs/2509.15436

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ماشین مش مندلی @manmaycry

7 months ago

@f14bertolotti Pay attention to this pre-print paper that decouples the receptive field from the depth of model arxiv.org/abs/2509.15436

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ماشین مش مندلی @manmaycry

7 months ago

@mariyaivasileva Can i ask you read a paper?

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ماشین مش مندلی @manmaycry

7 months ago

@Pseudo_Sid26 This paper introduces a new layer combining benefits of both: arxiv.org/abs/2509.15436

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Abiral Shakya @abiralxshakya

7 months ago

Unwrapping TPUs day 2 --> we explain TPU v1 architecture! come read about systolic arrays and neat memory buffering techniques

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ماشین مش مندلی @manmaycry

8 months ago

@Bubbly_B27 مرد واقعی h100 میخاد

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Ped @PedTalksSports

8 months ago

The goals have to actually lead to something

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ماشین مش مندلی @manmaycry

8 months ago

@bravo_abad arxiv.org/abs/2509.15436

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Probability and Statistics @probnstat

8 months ago

Neural Differential Equations treat neural networks as continuous-time systems where hidden states evolve according to differential equations. Instead of discrete layers, they integrate over time, offering memory efficiency and adaptive computation. In deep learning, they’re used for modeling time-series, physical systems, and dynamics in scientific data. Real-world uses include climate modeling, epidemiology, and motion prediction, where continuous evolution is natural. Image: share.google/jmA78rN5Qg4dZy…