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سایر جلسات از طریق همین کانال اطلاع رسانی خواهد شد

💠 Compositional Learning Journal Club

Join us this week for an in-depth discussion on Compositional Learning in the context of cutting-edge text-to-image generative models. We will explore recent breakthroughs and challenges, focusing on how these models handle compositional tasks and where improvements can be made.

✅ This Week's Presentation:

🔹 Title: Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by Step


🔸 Presenter: Amir Kasaei

🌀 Abstract:

This paper explores the use of Chain-of-Thought (CoT) reasoning to improve autoregressive image generation, an area not widely studied. The authors propose three techniques: scaling computation for verification, aligning preferences with Direct Preference Optimization (DPO), and integrating these methods for enhanced performance. They introduce two new reward models, PARM and PARM++, which adaptively assess and correct image generations. Their approach improves the Show-o model, achieving a +24% gain on the GenEval benchmark and surpassing Stable Diffusion 3 by +15%.


📄 Papers: Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by Step


Session Details:
- 📅 Date: Wednesday
- 🕒 Time: 2:15 - 3:15 PM
- 🌐 Location: Online at vc.sharif.edu/ch/rohban

We look forward to your participation! ✌️

Research Position at the  Sharif Center for Information Systems and Data Science:

We are seeking several highly skilled students for a project with a deadline  for the NeurIPS conference, focusing on predictive maintenance for batteries and bearings.
Candidates should have strong abilities in precise implementation and integrating new ideas into various architectures such as contrastive learning, transformers, PINN(Physics-informed neural networks) and diffusion models to rapidly enhance the research group's capabilities.

The project is under the direct collaboration of Dr. Babak Khalaj, Dr. Siavash Ahmadi, and Dr. Mohammad Hossein Rohban.

To apply and submit your CV, please contact via email: seyedreza.shiyade@gmail.com

Postdoctoral Research Position Available

The Robust and Interpretable Machine Learning (RIML) Lab at the Computer Engineering Department of Sharif University of Technology is seeking a number of highly motivated and talented postdoctoral researchers to join our team. The successful candidate will work on cutting-edge research involving Large Language Model (LLM) Agents.

• 1-2 years, with the possibility of extension based on performance and funding
• Conduct innovative research on LLM Agents
• Collaborate with a multidisciplinary team of researchers
• Publish high-quality research papers in top-tier conferences and journals
• Mentor graduate and undergraduate students
• Present research findings at international conferences and workshops

Qualifications:
• Ph.D. in Computer Science, Computer Engineering, or a related field earned at most in the last 2 years
• Strong background in natural language processing, machine learning, and artificial intelligence
• Experience with large language models and their applications
• Excellent programming skills (Python, and PyTorch, etc.)
• Strong publication record in relevant areas
• Excellent communication and teamwork skills

Interested candidates should submit the following documents to rohban@sharif.edu by Feb. 7th:
• A cover letter describing your research interests and career goals
• A detailed CV, including a list of publications
• Contact information for at least two references

For more information about our recent research topics, please check out my google scholar: https://scholar.google.com/citations?hl=en&user=pRyJ6FkAAAAJ&view_op=list_works&sortby=pubdate.

Research Assistant Position Available

The Robust and Interpretable Machine Learning (RIML) Lab at the Computer Engineering Department of Sharif University of Technology is seeking a number of highly motivated and talented research assistants to join our team to work on Large Language Model (LLM) Agents.

Qualifications:
• M.Sc. in Computer Science, Computer Engineering, or a related field earned at most in the last 2 years
• Strong background in natural language processing, machine learning, and artificial intelligence
• Experience with large language models and their applications
• Excellent programming skills (Python, and PyTorch, etc.)
• Excellent communication and teamwork skills

Interested candidates should submit the following documents to rohban@sharif.edu by Feb. 12th:

• A cover letter describing their research/career goals and why they are interested in this position.
• A detailed CV, including a list of publications

For more information about our recent research topics, please check out my google scholar: https://scholar.google.com/citations?hl=en&user=pRyJ6FkAAAAJ&view_op=list_works&sortby=pubdate.

💠 Compositional Learning Journal Club

Join us this week for an in-depth discussion on Compositional Learning in the context of cutting-edge text-to-image generative models. We will explore recent breakthroughs and challenges, focusing on how these models handle compositional tasks and where improvements can be made.

✅ This Week's Presentation:

🔹 Title: Correcting Diffusion Generation through Resampling


🔸 Presenter: Ali Aghayari

🌀 Abstract:
This paper addresses distributional discrepancies in diffusion models, which cause missing objects in text-to-image generation and reduced image quality. Existing methods overlook this root issue, leading to suboptimal results. The authors propose a particle filtering framework that uses real images and a pre-trained object detector to measure and correct these discrepancies through resampling. Their approach improves object occurrence by 5% and FID by 1.0 on MS-COCO, outperforming previous methods in generating more accurate and higher-quality images.


📄 Papers: Correcting Diffusion Generation through Resampling


Session Details:
- 📅 Date: Tuesday
- 🕒 Time: 5:30 - 6:30 PM
- 🌐 Location: Online at vc.sharif.edu/ch/rohban

We look forward to your participation! ✌️

🚀 Join Richard Sutton’s Talk at Sharif University of Technology

🎙 Title: The Increasing Role of Sensorimotor Experience in Artificial Intelligence
👨‍🏫 Speaker: Rich Sutton (Keen Technologies, University of Alberta, OpenMind Research Institute)
📅 Date: Wednesday
🕗 Time: 8 PM Iran Time
💡 Sign Up Here: https://forms.gle/q1M7qErWvydFxR9m6

🎥 فیلم جلسه اول درس System 2
🔸 موضوع: Introduction & Motivation
🔸 مدرسین: دکتر رهبان و آقای سمیعی
🔸 تاریخ: ۲۱ بهمن ۱۴۰۳
🔸لینک‌ یوتیوب
🔸 لینک آپارات

🚀 We will be live from 19:45. Join us here:
https://www.youtube.com/watch?v=Y4UZNc4eh4U

🎙 Title: The Increasing Role of Sensorimotor Experience in Artificial Intelligence
👨‍🏫 Speaker: Rich Sutton (Keen Technologies, University of Alberta, OpenMind Research Institute)

🚀 Join Michael Littman’s Talk at Sharif University of Technology

🎙 Title: Assessing the Robustness of Deep RL Algorithms
👨‍🏫 Speaker: Michael Littman (Brown University, Humanity-Centered Robotics Initiative)
📅 Date: Friday (Feb 21, 2025)
🕗 Time: 5:30 PM Iran Time
💡 Sign Up Here: https://forms.gle/amgtsGrDVn4mdRai9

🚀 Join Chris Watkins’s Talk at Sharif University of Technology

🎙 Title: From Shortest Paths to Value Iteration to Q-Learning
👨‍🏫 Speaker: Chris Watkins (Professor of Computer Science, Royal Holloway)
📅 Date: Friday (Feb 21, 2025)
🕗 Time: 3:00 PM Iran Time
💡 Sign Up Here: https://forms.gle/ET3Y5jB6Jt9vkQ2x9

@DeepRLCourse

🚀 Join Peter Stone’s Talk at Sharif University of Technology

🎙 Title: Multiagent RL: Cooperation and Competition
👨‍🏫 Speaker: Peter Stone (Professor of Computer Science, University of Texas at Austin)
📅 Date: Thursday (Feb 27, 2025)
🕗 Time: 3:30 PM Iran Time
💡 Sign Up Here: https://forms.gle/M4QxTUWimGyvUmPv7

@DeepRLCourse

🔥 Open Position: Research Intern/Collaborator – Virtual Staining of Histopathology Images

🔸 Join our CVPR conference paper project on Virtual Staining!
We are looking for dedicated researchers, with a preference for local candidates, as this role requires 20 hrs/week of in-person collaboration.

🔸 Technical Requirements:
💠 Strong English reading & writing skills for technical documentation.
💠 Hands-on experience with:
    🌀 PyTorch & deep learning fundamentals
    🌀 Running & troubleshooting GitHub repositories
    🌀 Exposure to generative models (GANs, diffusion models) is a plus!
    🌀 Ability to write clean, organized Python code

🔸 Non-Technical Requirements:
💠 Commitment to 20 hrs/week in-person work at our lab
💠 Persistence in solving technical challenges (e.g., debugging model training)
💠 Strong teamwork & communication skills
💠 Curiosity about medical imaging & generative AI

🔸 Why Join?
💠 Mentorship from Dr. Rohban & the RIML Lab team
💠 Hands-on experience with generative models (GANs/Diffusion) for medical imaging
💠 Work with collaborative coding (GitHub) & Linux-based workflows
💠 Opportunity for CVPR-tier co-authorship & strong recommendation letters

📩 How to Apply
The deadline for submission has already passed

🚀 Join Jakob Foerster’s Talk at Sharif University of Technology

🎙 Title: Reinforcement Learning at the Hyperscale!
👨‍🏫 Speaker: Jakob Foerster (Associate Professor, University of Oxford)
📅 Date: Friday (Mar 7, 2025)
🕗 Time: 1:00 PM Iran Time
💡 Sign Up Here: https://forms.gle/HYDizuvMkxVGA5hu7

@DeepRLCourse

کد تخفیف ۵۰ درصدی مخصوص اعضای کانال:
RIMLLab

🚀 Open Research Position: Hallucination Detection & Mitigation in Vision-Language Models (VLMs)

We are looking for motivated students to join our research on hallucination detection and mitigation in Visual Question Answering (VQA) models at RIML Lab.

🔍 Project Description
Visual Question Answering (VQA) models generate text-based answers by analyzing an input image and a query. Despite their success, they still suffer from hallucination issues, where responses are incorrect, misleading, or not grounded in the image content.
This research focuses on detecting and mitigating these hallucinations to enhance the reliability and accuracy of VQA models.

📄 Relevant Papers
"Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding"
"CODE: Contrasting Self-generated Description to Combat Hallucination in Large Multi-modal Models"
"Alleviating Hallucinations in Large Vision-Language Models through Hallucination-Induced Optimization"

🔹 Must-Have Requirements
- Strong Python programming skills
- Knowledge of deep learning (especially VLMs)
- Hands-on experience with PyTorch
- Ready to start immediately

⏳ Workload
Commitment: At least 20 hours per week


📌 Note: Filling out this form does not guarantee acceptance. Only shortlisted candidates will receive an email notification.

📅 Application Deadline: March 28, 2025
🔗 Apply here: Google Form

🛑 This position is now closed. Shortlisted candidates have been notified by March 30, 2025. Thank you to everyone who applied! Stay tuned for future opportunities.

📧 For inquiries: iamirezzati@gmail.com
💬 Telegram: @amirezzati

@RIMLLab
#research_position #ML_research #DeepLearning #VQA

گروه Geometric Deep Learning به مباحث مرتبط با اشیاء هندسی می‌پردازد. پیش‌بینی رفتار پروتئین‌ها یا مولکول‌های شیمیایی که ساختار هندسی‌شون در اینکه چطوری در عمل رفتار می‌کنن مؤثره. مباحث تئوری مرتبط باهاش یه چیزی میان گراف و جبر و دیپ لرنینگ هست.
تحت نظر آقای سید محمد حسینی، دانشجوی دکترای مشترک دکتر جعفری و دکتر رهبان

https://docs.google.com/forms/d/e/1FAIpQLSd8FEHOmscpFo6R2SEA5LbFQ8fyM518yXIe07G29XYLk6Rgzg/viewform

پست لینکدین دکتر رهبان

نوروز باستانی و سال جدید با آغاز بهار شروع شد. در سال گذشته موقعیت‌های بسیاری را با دانشجویانم و اعضای آزمایشگاه RIML دور یکدیگر جمع گشتیم و جشن گرفتیم. من معتقدم که دانشجویان مهم‌ترین سرمایه‌های این کشور هستند و ما باید قدر ایشان را بیشتر بدانیم.
در سال گذشته ما در آزمایشگاه سعی داشتیم تا در جهت حل مشکلات کشور و علم قدم برداریم و در جهت بهتر کردن زندگی انسان‌ها تلاش کنیم. همچنین ۱۰ مقاله در کنفرانس‌ها و ژورنال‌های برتر CVPR, ICLR, ICML, NeurIPS, TMLR, ECCV با موضوع اعتمادپذیری در یادگیری ماشین منتشر کردیم.
همچنین در برگزاری ورکشاپ Spurious Correlation and Shortcut Learning در کنفرانس ICLR2025 که برای اولین بار توسط یک تیم از ایران در یکی از کنفرانس‌های برتر هوش مصنوعی اتفاق می‌افتد، تلاش کردیم.
همچنین اعضای آزمایشگاه نقش مهم و پررنگی در برگزاری مسابقه بین‌المللی هوش مصنوعی RAYAN داشتند.
ممنون از تیم فوق‌العاده، همکاران و حمایت‌کنندگان که این سال را الهام‌بخش و فوق‌العاده کردند.