سلام دوستان،
امیدوارم خوب باشین...

تیم AiCrowd مسیر خوبی رو شروع کرده و به درد دوستانی که در زمینه های مربوط به داده و هوش مصنوعی کار کردن میخوره و میتونه فرصت خیلی خوبه باشه. پیام زیر رو با دقت بخونین و نسخه اولیه سایت رو ببینین:



سلام به همه دوستان و متخصصین #هوش_مصنوعی، #علم_داده و #یادگیری_ماشین ✋

ما در تلاشیم تا با ارائه پروژه‌ها و مسائل واقعی مجموعه‌های معتبر (که به ما برای حل مسائلشون اعتماد کردن) اون‌ها رو از طریق #نوآوری_باز و #جمع_سپاری حل کنیم و به این ترتیب به توزیع عادلانه‌تر فرصت‌ها و درآمدها بین متخصصین، تیم‌ها و شرکت‌ها (خصوصا تیم‌ها و شرکت‌های کوچک‌تر) کمک کنیم.
شما هم می‌تونید در صورت تمایل به گرفتن پروژه‌ها و مشارکت در اجرای اون‌ها از طریق لینک زیر اقدام کنید: 🤝
https://aicrowd.ir



موفق باشید،
ایمان

اگه سوالی هم داشتین میتونین به من پیام بدین:
@IKJ1992

✅ فرآخوان شناسایی شرکت های توانمند در حوزه طراحی و توسعه سامانه گفتگوی محاوره‌ای (Chit-Chat) و تولید مجموعه دادگان چیت چت همراه اول

مرکز تحقیق و توسعه شرکت ارتباطات سیار ایران (همراه اول)، درصدد شناسایی شرکت ­های توانمند در زمینه طراحی و توسعه سامانه گفتگوی محاوره‌ای (Chit-Chat) و تولید مجموعه دادگان چیت چت همراه اول است. متقاضیان می­توانند در صورت برخورداری از تخصص و دانش فنی در حوزه مذکور، نسبت به ارائه مستندات محصولات و قابلیت ­های شرکت اقدام نمایند.


https://tamin.mci.ir/#/articles/details/9fcf04a2-df26-47fb-bc6c-20cbb078a556


@irandeeplearning

✅ مجموعه سخنرانی های هوش مصنوعی (بررسی آخرین تحولات و دستاوردهای حوزه هوش مصنوعی)

⏱ چهارشنبه 3 اسفند ماه 1401

📌 دانشگاه صنعتی امیرکبیر

🔗 https://aaic.aut.ac.ir/workshop/2

🆔 @aaic_aut

We at OIST are seeking a highly qualified and motivated postdoctoral researcher in the field of computer vision or machine learning to join our dynamic research team. The successful candidate will have the opportunity to work on cutting-edge projects and contribute to the advancement of our research in this field.

Candidates should have a Ph.D. in computer science, electrical engineering, or a related field, and a strong background in machine learning or computer vision. Experience with deep learning frameworks such as TensorFlow and PyTorch is highly desirable.

The position offers a competitive salary, access to state-of-the-art equipment, and opportunities for professional development.

To apply, please send your CV to Mohammad Sabokrou at mohammad.sabokrou@gmail.com or visit our OIST MLDS unit page at https://groups.oist.jp/mlds or OIST: https://youtube.com/watch?v=OLeylXbZDpo&authuser=0 for more information.

✅ «گفت‌وگوی آنلاین آشنایی با ChatGPT و کاربردهای آن»

زمان برگزاری :
امروز(دوشنبه) ساعت 18:30 الی 20:30

لینک آنلاین:
https://www.skyroom.online/ch/academyh/chatgpt

@hamrah_academy
@irandeeplearning

🎥 A live broadcast of GPT-4 (Tonight from 23:30 Tehran time zone!)

👤 Presenter: Greg Brockman, President and Co-Founder of OpenAI

🗒 Content: Demo showcasing GPT-4 and some of its capabilities/limitations

🌐 https://www.youtube.com/watch?v=outcGtbnMuQ

📌 #GPT4 is a large multimodal model (accepting image and text inputs, emitting text outputs)
🔗 https://openai.com/product/gpt-4

@irandeeplearning

📣 هفتمین دوره مسابقات هوش مصنوعی امیرکبیر
📆 اردیبهشت ۱۴۰۲، دانشگاه صنعتی امیرکبیر

💢عناوین مسابقات💢
🔹 پیش‌بینی بازار سهام
🔹 تشخیص عملیات مشکوک بانکی
🔹 جستجوی کلمات کلیدی در مکالمات مرکز تماس همراه اول
🔹 تشخیص مقصود اصلی کاربر در جملات طولانی و پیچیده در ربات‌های گفتگوی همراه اول

✅ جهت کسب اطلاعات بیشتر و شرکت در مسابقات به وب‌سایت زیر مراجعه کنید:

🌐 https://aaic.aut.ac.ir
🆔 @aaic_aut

عضویت در انجمن انفورماتیک ایران، کمیته هوش مصنوعی

https://www.linkedin.com/posts/imankhanijazani_aevaewaecabraetaedaeuaewaehahy-aepaeuaeuaetaeu-activity-7051425854752481280-ulpG

🛎🛎🛎 دعوت برای سخنرانی در کمیته هوش مصنوعی انجمن انفورماتیک ایران

https://www.linkedin.com/posts/imankhanijazani_aevaewaecabraetaedaeuaewaehahy-aehaesaetabraexaepaexaev-activity-7072159216819412992-xoLG?utm_source=share&utm_medium=member_android

دوره آموزشی تابستانه هوش مصنوعی پروژه محور
اطلاعات بیشتر : https://scs.ipm.ac.ir/ais.jsp

@irandeeplearning

🎥 آموزش شبکه های عصبی گرافی

https://class.vision/product/graph-neural-network/

سرفصلهای دوره | اسلایدها | ویدیوی معرفی | کدها | فصل اول به عنوان نمونه ویدیو | کاربرد شبکه های عصبی گرافی

این آموزش در 7 فصل و شامل مباحث تئوری+ عملی بوده و 13 کد در فریم ورک تنسرفلو و پایتورچ جئومتریک مورد بحث قرار گرفته است.

💳کد تخفیف 20 درصدی ویژه اعضای کانال:
irandeeplearning

✅ چهارمین دوره‌ «رویداد هوش مصنوعی امیرکبیر AAISS» شامل دو بخش سخنرانی علمی و کارگاه های آموزشی

👤دعوت از محققین مراکز دانشگاهی بنام داخلی و خارجی EPFL، Alberta، ETH، Monash، Illinois, OIST, UCI, Waterloo, Ottawa, Caltech, Hong Kong, McGill, Western, UCSD, USC, AUT, TMU, IUST و شرکت های بزرگ نظیر Google، Microsoft، eBay، Netflix, Huawei, Snapp

⚡️مناسب برای تمامی علاقه‌مندان هوش‌مصنوعی

⏱ زمان برگزاری از ۱۵ لغایت ۲۵ آذرماه

🌐 ثبت نام و کسب اطلاعات بیشتر:
https://aaiss.ir

🕑 برنامه زمانبندی ارائه ها:
https://aaiss.ir/schedule

🆔 کانال اطلاع رسانی رویداد: @aaiss_aut

💠کد تخفیف ویژه:‌ IranDeepLearning


@irandeeplearning
@ceit_ssc

If you're interested in self-supervised learning, join our meeting today at 9:30 a.m. Iran time!
Zoom: https://oist.zoom.us/j/92257619030?pwd=d2IvVktjQUVPME8rdFhqWFlmNERRQT09
Meeting ID: 922 5761 9030
Passcode: 595720
Speaker: Dr. Yuki M. Asano, Assistant Professor, QUVA Lab, University of Amsterdam
Title: Self-Supervised Learning from Images and Videos using Optimal Transport
Abstract:
In this talk we will learn more about self-supervised learning -- the principles, the methods and how properly utilizing video data will unlock unprecendented visual performances.
I will first provide a brief overview of self-supervised learning and show how clustering can be combined with representation learning using optimal transport ([1] @ ICLR'20 spotlight). Next, I will show how this method can be generalised to multiple modalities ([2] @NeurIPS'20) and for unsupervised segmentation in images ([3] @CVPR'22) and in videos ([4] @ICCV'23). Finally, I show how optimal transport can be utilized to learn models from scratch from just a single Walking Tour video that outperform those trained on ImageNet, demonstrating high potential for future video-based embodied learning ([5] @ICLR'24). 
[1] Self-labelling via simultaneous clustering and representation learning
Asano, Rupprecht, Vedaldi.  ICLR, 2020
[2] Labelling unlabelled videos from scratch with multi-modal self-supervision. Asano, Patrick, Rupprecht, Vedaldi. NeurIPS 2020
[3] Self-supervised learning of object parts for semantic segmentation. Ziegler and Asano. CVPR 2022
[4] Time Does Tell: Self-Supervised Time-Tuning of Dense Image Representations. Salehi, Gavves, Snoek, Asano.
[5] Is ImageNet worth 1 video? Learning strong image encoders from 1 long unlabelled video. Venkataramanan, Rizve, Carreira, Avrithis, Asano. ICLR 2024.
 

This talk will start very soon, please join if you are interested

Graph Convolutional Networks:
Unleashing the power of Deep Learning for Graph data

🗓زمان برگزاری (به صورت آنلاین): شنبه 28 بهمن ماه 1402
⏱ساعت 17:30 الی 19

📍آدرس اتاق مجازی: https://vc.sharif.edu/ch/cognitive


@irandeeplearning | @cvision

If you're interested in federated learning, particularly in medical imaging, we invite you to join our seminar tomorrow (Friday) at 11:00 a.m. Iran time! Zoom: https://oist.zoom.us/j/95908496615?pwd=akxZNmprLzNXY212TFh0ZWQ1ZlNyUT09
Meeting ID: 959 0849 6615
Passcode: 767685

Speaker: Prof. Shadi Albarqouni, Computational Medical Imaging Research, University of Bonn

Title: Unlocking the Potential of Federated Learning in Medical Imaging


Abstract: Deep Learning (DL) stands at the forefront of artificial intelligence, revolutionizing computer science with its prowess in various tasks, especially in computer vision and medical applications. Yet, its success hinges on vast data resources, a challenge exacerbated in healthcare by privacy concerns. Enter Federated Learning, a groundbreaking technology poised to transform how DL models are trained without compromising data security. By allowing local hospitals to share only trained parameters with a centralized DL model, Federated Learning fosters collaboration while preserving privacy. However, hurdles persist, including heterogeneity, domain shift, data scarcity, and multi-modal complexities inherent in medical imaging. In this illuminating talk, we delve into the clinical workflow and confront the common challenges facing AI in Medicine. Our focus then shifts to Federated Learning, exploring its promise, pitfalls, and potential solutions. Drawing from recent breakthroughs, including a compelling MR Brain imaging case study published in Nature Machine Intelligence, we navigate the landscape of secure and efficient AI adoption in healthcare.


Bio: Shadi Albarqouni, a pioneering figure in Computational Medical Imaging, serves as a Professor at the University of Bonn and an AI Young Investigator Group Leader at Helmholtz AI. With significant roles at Imperial College London, ETH Zurich, and the Technical University of Munich (TUM), Shadi's impact reverberates through his 100+ publications in esteemed journals and conferences. His expertise extends beyond academia, with contributions as an Associate Editor at IEEE Transactions on Medical Imaging and evaluator for national and international grants like DFG, BMBF, and EC. Recognized with awards like the DAAD PRIME Fellowship, Shadi fosters collaboration through AGYA and ELLIS memberships and initiatives like the Palestine Young Academy and the RISE-MICCAI community, focusing on innovative medical solutions and knowledge transfer to emerging countries. Explore more about his work at https://albarqouni.github.io/.

We will be commencing in the next 30 minutes. If you are interested, please feel free to join us.

If you're interested in federated learning, particularly in medical imaging, we invite you to join our seminar tomorrow (Friday) at 11:00 a.m. Iran time! Zoom: https://oist.zoom.us/j/95908496615?pwd=akxZNmprLzNXY212TFh0ZWQ1ZlNyUT09
Meeting ID: 959 0849 6615
Passcode: 767685

Speaker: Prof. Shadi Albarqouni, Computational Medical Imaging Research, University of Bonn

Title: Unlocking the Potential of Federated Learning in Medical Imaging


Abstract: Deep Learning (DL) stands at the forefront of artificial intelligence, revolutionizing computer science with its prowess in various tasks, especially in computer vision and medical applications. Yet, its success hinges on vast data resources, a challenge exacerbated in healthcare by privacy concerns. Enter Federated Learning, a groundbreaking technology poised to transform how DL models are trained without compromising data security. By allowing local hospitals to share only trained parameters with a centralized DL model, Federated Learning fosters collaboration while preserving privacy. However, hurdles persist, including heterogeneity, domain shift, data scarcity, and multi-modal complexities inherent in medical imaging. In this illuminating talk, we delve into the clinical workflow and confront the common challenges facing AI in Medicine. Our focus then shifts to Federated Learning, exploring its promise, pitfalls, and potential solutions. Drawing from recent breakthroughs, including a compelling MR Brain imaging case study published in Nature Machine Intelligence, we navigate the landscape of secure and efficient AI adoption in healthcare.


Bio: Shadi Albarqouni, a pioneering figure in Computational Medical Imaging, serves as a Professor at the University of Bonn and an AI Young Investigator Group Leader at Helmholtz AI. With significant roles at Imperial College London, ETH Zurich, and the Technical University of Munich (TUM), Shadi's impact reverberates through his 100+ publications in esteemed journals and conferences. His expertise extends beyond academia, with contributions as an Associate Editor at IEEE Transactions on Medical Imaging and evaluator for national and international grants like DFG, BMBF, and EC. Recognized with awards like the DAAD PRIME Fellowship, Shadi fosters collaboration through AGYA and ELLIS memberships and initiatives like the Palestine Young Academy and the RISE-MICCAI community, focusing on innovative medical solutions and knowledge transfer to emerging countries. Explore more about his work at https://albarqouni.github.io/.