📢یادآوری

⬅️برای حضور در میزگرد مجازی امشب از طریق لینک زیر اقدام کنید و به عنوان مهمان وارد کلاس شوید:

🔗https://vc.sharif.edu/ch/wss-sharif

▪️▪️▪️▪️▪️▪️▪️▪️▪️▪️
🌐http://wss-sharif.com
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🌐Instagram      🌐LinkedIn
🌐YouTube         🔗Twitter
☃️@WSS_SUT

💬معرفی میزگرد سوم

📆۱۱ اسفندماه
🕰ساعت ۲۲
⚙️علمی
💻مجازی

*📌*موضوع میزگرد
⬅️هوش مصنوعی، چالش‌ها و فرصت‌ها: مدل‌های زبانی بزرگ و مدل‌های چندحالتی؛ تاریخچه، امکانات، محدودیت‌ها و چشم‌انداز

📆March 1
🕰Starts at 22
⚙️Scientific
💻Virtual

*📌*Roundtable Topic
➡️Artificial Intelligence, Challenges, and Opportunities: Large Language and Multi-Modal Models; History, Abilities, Limitations, and Future

🔹🔸🔹🔸🔹🔸🔹🔸🔹🔸

👤Invitees | مهمانان
👩Mahdieh Soleymani | Associate Professor @ Sharif University of Technology
👩Abbas Hosseini | Chief AI Officer @ Tapsage
👨Abbas Mehrabian | Researcher @ Google Deepmind

#Roundtable
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🌐http://wss-sharif.com
🔗Zilink 🌐Telegram
🌐Instagram 🌐LinkedIn
🌐YouTube 🔗Twitter
☃️@WSS_SUT

«دستیاری آموزشی درس بازیابی پیشرفته اطلاعات» ⭕️ با سلام؛ در صورتی که تمایل دارید در نیم‌سال آینده (نیم‌سال دوم ۰۲-۰۳) دستیار آموزشی درس بازیابی پیشرفته اطلاعات دکتر سلیمانی باشید، درخواست خود را از طریق این فرم تا ۶ بهمن ثبت نمایید.

«دستیاری آموزشی درس بازیابی پیشرفته اطلاعات»

⭕️ با سلام؛
در صورتی که تمایل دارید در نیم‌سال آینده (نیم‌سال دوم ۰۲-۰۳) دستیار آموزشی درس بازیابی پیشرفته اطلاعات دکتر سلیمانی باشید، درخواست خود را از طریق این فرم تا ۶ بهمن ثبت نمایید.

«دستیاری درس یادگیری ژرف»

⭕️ با سلام؛
در صورتی که تمایل دارید در نیم‌سال آینده (نیم‌سال دوم ۰۲-۰۳) دستیار آموزشی درس یادگیری ژرف دکتر سلیمانی باشید، درخواست خود را از طریق فرم زیر ثبت نمایید:

https://forms.gle/geJ5KqUEmo8yZCSV6

We are seeking an intern/collaborator to join us in an exciting project focused on advancing reinforcement learning through enhanced credit assignment. The primary objective of this project is to improve the core algorithms of RL by tackling the challenge of credit assignment.

Technical Requirements:
- Good English proficiency to understand and comprehend academic papers.
- Hands-on experience in ML and Deep Learning.
- Experience with PyTorch.
- Ability to collaborate with a team using GitHub.

Non-Technical Requirements:
- Dedication and persistence.
- Enthusiasm for learning

Expectations:
- Benefit from the support and collaborative atmosphere of the MLL under the guidance of Prof. Solyemani.
- Possibility of receiving a research-related recommendation letter from Prof. Solyemani.
- Potential opportunity to have a paper accepted at a top-tier conference

This is a shared work of @sfumars lab and @sutmll, under the supervision of Prof. Chen and Dr. Soleymani. Please note that the aforementioned requirements and expectations are intended to be flexible and supportive rather than stringent. We encourage talented individuals with a passion for reinforcement learning to apply. We look forward to receiving your application and potentially working together on this exciting project.

If you’re interested, you can send your CV to razavii.roozbeh@gmail.com

#open_position
We are looking for a new intern to work on novel high-quality image generation methods. The work is consisted of a study on text-aligned and diverse image generation along with the corresponding evaluation metrics. This is a shared work in @sutmll and @RIMLLab, under the supervision of Dr. Soleymani and Dr. Rohban.

## Requirements
- Familiarity with ML and DL fundamentals
- Familiarity with transformers
- Familiarity with diffusion models
- Familiarity with PyTorch framework
- Having skills in reading and presenting papers

## Preferred Qualifications
- Familiarity with vision-language models (VLMs)

## Benefits
- Being supportively supervised by  MLL and RIML's PIs and graduate students
- Being involved in cutting-edge research and study
- Receiving recommendation letters from Dr. Soleymani and Dr. Rohan

## How to Apply
To apply for the position, please send your CV and transcript, as well as a research statement, describing how you fulfill the position's requirements to ghaznavi.mahdi@gmail.com, aliabdollahi024a@gmail.com, and arashmarioriyad@gmail.com. Besides, please complete the following task, send the final results, and a report about the way you obtained them with the application:
Use text-to-image generative models (diffusion-based are recommended) to generate 5 high-quality images of a scene with the following description: "A man is driving and a woman is sitting beside him". The images should be as realistic and diverse as possible.

## Application Deadline
Send your application by Sunday, 1402/10/3.

## Contact Information
If you have any questions, please contact the above email or PM Ali Abdollahi (https://t.me/mllresponse).

#research_application

Telegram

response

You can contact @mllresponse right away.

Visual Compositional Generation Research Application This is a shared research work between Dr. Soleymani (MLL) and Dr. Rohban (RIML Lab) from Sharif University of Technology on Visual Compositional Generation in Diffusion-based Text-to-image Models with…

Visual Compositional Generation Research ApplicationThis is a shared research work between Dr. Soleymani (MLL) and Dr. Rohban (RIML Lab) from Sharif University of Technology on Visual Compositional Generation in Diffusion-based Text-to-image Models with a goal of submitting a survey on the related problems in a 3-month milestone.

Project Description:Large-scale diffusion-based models have achieved state-of-the-art results on text-to-image (T2I) synthesis tasks. Despite their significant ability of generating high quality and prompt-aligned images, these models are exposed to a number of major compositional-related drawbacks such as object missing and improper attribute binding.
Hence, we aim to study and categorize the compositional-related failure modes and the corresponding methods to overcome the problems.
For more information you can read the following papers.
- T2I-CompBench: A Comprehensive Benchmark for Open-world Compositional Text-to-image Generation- Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models- Training-Free Structured Diffusion Guidance for Compositional Text-to-Image SynthesisThe Musts:- Being familiar with Linear Algebra fundamentals is necessary.
- Being familiar with Statistics and Probability fundamentals is necessary.
- Being familiar with Machine Learning and Deep Learning fundamentals is necessary.
- Being familiar with Transformers (Attention Mechanism) and Diffusion Models is recommended (Note that there will be some short training sessions on the mentioned topics during the project).
- Dedicating considerable time to the project is necessary.

Process:
- Deadline of submitting the application: 10/24/2023 - 23:59 (Tehran Time)
- First wave of rejection/acceptance: 10/25/2023 - 23:59 (Tehran Time)
- Short Interviews: 10/26/2023 - 23:59 (Tehran Time)
- Final wave of rejection/acceptance: 10/27/2023 - 23:59 (Tehran Time)

Please fill this form till 10/24/2023 - 23:59 (Tehran Time). Note that the deadlines are somehow harsh and it's important to dedicate as much time as possible.

https://docs.google.com/forms/d/e/1FAIpQLSfRSteyD6y0LUF2RI4GiMa3Bv_N8w_EI5OTSt3F0j1teAMHSA/viewform?usp=sf_link

We would be happy to answer any questions you may have through arashmarioriyad@gmail.com.

@sutmll
#research_application

هم‌اکنون می‌توانید برای اطلاع از فعالیت‌های علمی و پژوهشی آزمایشگاه ما را در توییتر دنبال نمایید.

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