Tensorflow(@CVision) | United States America (US)

Create: 2024-11-07 Update: 2025-07-06 01:30:51

توی ویدیو بالا توضیح میده که در مقایسه با مدل‌های دیگه، برای انجام وظیفه‌ ای مثل هدایت خودرو، مدل LTCN تنها به حدود ۱۹ نورون نیاز داره، در حالی که سایر مدل ‌ها برای همون کار به هزاران نورون نیاز دارن.

از نکات مهم ارایه شده در این کار میشه به موارد زیراشاره کرد :

مدل می ‌تونه همون نتایج شبکه‌های عصبی دیگه رو با ۱۰ تا ۲۰ برابر نورون کمتر به دست بیاره

این مدل روابط علی و معلولی دنیای واقعی رو یاد می‌گیره

توانایی فوق ‌العاده‌ای در تعمیم خارج از محدوده‌ داده‌های آموزشی داره، یعنی میتونه همون وظیفه رو توی شرایط کاملا متفاوت انجام بده

بدون نیاز به تعیین اهداف مشخص میتونه کار کنه

و در نهایت توی یادگیری تقلیدی بسیار خوب عمل می ‌کنه

در واقع نوآوری کلیدی این مدل اینه که امکان تغییر سرعت هر نورون رو به صورت متغیر و لحظه‌ ای فراهم میکنه.

همین ویژگی ساده باعث شده که این شبکه‌ها به نتایج فوق‌العاده‌ای برسن. سخنرانی تد رو برای توضیحات بیشتر ببینید. نوشتن در موردش زمان زیادی می‌بره

ادامه دارد ...

https://www.ted.com/talks/ramin_hasani_liquid_neural_networks

Tensorflow(@CVision)

کار دیگه ای که شباهت هایی به مقاله قبلی داره کار آقای رامین حسنی از MIT هست. عنوان مقاله Liquid Time Constant Networks هست. این مدل در واقع نوعی شبکه عصبی پویا و انعطاف ‌پذیره که برای پردازش اطلاعات در زمان و تصمیم ‌گیری در شرایط پیچیده با الهام از ساختارهای…

hottg.com/cvision/3802

2.7K viewsAlister ☄️, edited Nov 7, 2024 at 18:34

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Tensorflow(@CVision)

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توی ویدیو بالا توضیح میده که در مقایسه با مدل‌های دیگه، برای انجام وظیفه‌ ای مثل هدایت خودرو، مدل LTCN تنها به حدود ۱۹ نورون نیاز داره، در حالی که سایر مدل ‌ها برای همون کار به هزاران نورون نیاز دارن.

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