Machine learning books and papers

سلام فقط دو روز تا سابمیت نهایی این مقاله باقی مونده دوستانی که نیاز دارن بهم پیام بدن. @Raminmousa1

🔥 OmniReasoning: Pushing the Limits of Audio-Visual Joint Reasoning 💡 Summary 🔗 Links: GitHub: https://github.com/huggingface arXiv: https://arxiv.org/abs/2609.39490 PDF: https://arxiv.org/pdf/2609.39490 Project Page: https://pku-value-lab.github.io/OmniReasoning-Homepage/ 📊 Datasets citing this paper: https://huggingface.co/datasets/mjuicem/OmniReasoning-131K https://huggingface.co/datasets/mjuicem/OmniReasoningBench @Machine_learn

با عرض سلام این مقاله نفرات ۲ و ۳ رو میخواییم اسم بنویسم. هزینه به ترتیب ۳۰۰ و ۲۰۰ دلار. اگه کسی نیاز داشت به ایدیم پیام بده. @Raminmousa1

با عرض سلام این مقاله نفرات ۲ و ۳ رو میخواییم اسم بنویسم. هزینه به ترتیب ۳۰۰ و ۲۰۰ دلار. اگه کسی نیاز داشت به ایدیم پیام بده. @Raminmousa1

🔥 Prism: Dynamic Sparse Attention for Native 2K Joint Video-Audio Generation Model Training 🔗 Links: GitHub: https://github.com/huggingface arXiv: https://arxiv.org/abs/2610.05416 PDF: https://arxiv.org/pdf/2610.05416 Project Page: https://francis-rings.github.io/Prism/ 🤖 Models citing this paper: https://huggingface.co/FrancisRing/Prism 🚀 Spaces citing this paper: https://huggingface.co/spaces/bibibi12345/Prism https://huggingface.co/spaces/tardellirs/model-pulse @Machine_learn

🔥 Spatial Memory Intelligence: Endowing World Models with Understanding-Driven Long-Term Memory 🔗 Links: GitHub: https://github.com/huggingface arXiv: https://arxiv.org/abs/2610.02521 PDF: https://arxiv.org/pdf/2610.02521 Project Page: https://spatial-memory-intelligence.github.io/ @Machine_learn

High-Impact Research Paper – MedicalRec / GROKRec Title: MedicalRec Price: $1,000 USD Looking for a ready-to-publish, novel research paper in medical AI and sustainable deep learning? This paper introduces GROKRec, an innovative recommender framework that uses embedding vectors from the Grok language model combined with numerical features to recommend the best deep learning model for any medical image classification task — without the need to train dozens of models on the target dataset. Key Highlights: Addresses major real-world problems: high computational cost, energy consumption, carbon emissions, and e-waste caused by training large DL models. Built on a newly curated public dataset MedicalRec-Bench II containing 3,500 research papers and over 6,000 model evaluation records across diverse medical imaging tasks. Evaluated under four feature configurations (MedicalRec I) using 13 different models. Achieves strong performance with HitRate@100 ranging from 72.43% to 77.08% — the highest among compared approaches. Uses composite loss functions and regularization techniques for accurate recommendations. Fully eliminates the trial-and-error process of training multiple models, significantly reducing carbon footprint. This is a complete, self-contained research contribution with a novel dataset and a practical, environmentally conscious solution for the medical AI community. Ideal for: Researchers, academic publishers, journals, or institutions looking for high-quality, ready-to-use work in medical image analysis, recommender systems, and green AI. Price: $1,000 USD (one-time transfer of ownership/rights as agreed). Interested? Contact me for the full manuscript, dataset details, or to discuss terms. @Raminmousa1

🔥 FrameMorrow: Future-guided Frame Selection with Prospective Tokens for Long-Horizon Video Generation 🔗 Links: GitHub: https://github.com/huggingface arXiv: https://arxiv.org/abs/2609.38839 PDF: https://arxiv.org/pdf/2609.38839 Project Page: https://yinbo0927.github.io/FrameMorrow/ @Machine_learn

🔥 TAPNext: Tracking Any Point (TAP) as Next Token Prediction 🔗 Links: GitHub: https://github.com/huggingface arXiv: https://arxiv.org/abs/2504.05579 PDF: https://arxiv.org/pdf/2504.05579 Project Page: https://tap-next.github.io/ @machine_learn

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📖 "A Little Book on the Fundamentals of Generative AI" - an intuitive introduction to the mathematics: arxiv.org/pdf/2605.29713 #GenerativeAI #Mathematics #DeepLearning #AIResearch #MachineLearning #arXiv ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk @Machine_learn

🔎 #وبینار عملی ریشه‌یابی خطا با Sentry 🔮 در وبینار «از گزارش خطای کاربر تا ریشه‌یابی مشکل با Sentry» با یک سناریوی عملی، مسیر رسیدن از گزارش یک مشکل به سرنخ‌های لازم برای ریشه‌یابی آن را دنبال می‌کنیم. در این وبینار: • #سنتری را راه‌اندازی می‌کنیم و به یک اپلیکیشن متصل می‌شویم • یک خطا را ثبت می‌کنیم و با اطلاعاتی که Sentry در اختیارمان می‌گذارد، علت آن را بررسی می‌کنیم • با Session Replay اتفاقات پیش از بروز خطا را از دید کاربر بازبینی می‌کنیم • با Trace و Span مسیر اجرای عملیات را دنبال و گلوگاه‌های عملکردی اپلیکیشن را پیدا می‌کنیم. 📣 ارائه‌دهنده: علیرضا بانشی | SaaS Team Lead در هم‌روش 📅 سه‌شنبه ۲۱ مهر ساعت ۱۹ 🔗 ثبت‌نام رایگان: https://hmrv.sh/EdN7gd ☁️@hamravesh

با عرض سلام این مقاله به صورت کامل واگذار میشه به همراه پیاده سازی مجموعه داده و قالب latex. هزینه کار ۱۰۰۰ دلار @Raminmousa1

Understanding Attention From Q, K, V to Modern Transformer Attention https://drive.google.com/file/d/1fCHQ5xCQJ6jZszAYf-qP3VIySbzFIEDv/view @Machine_learn

Uniface Automate face detection, recognition, and analysis of key facial landmarks with the Uniface Python library. https://github.com/yakhyo/uniface @Machine_learn

For Sale: High-Impact Research Paper – MedicalRec / GROKRec Title: MedicalRec Price: $1,000 USD Looking for a ready-to-publish, novel research paper in medical AI and sustainable deep learning? This paper introduces GROKRec, an innovative recommender framework that uses embedding vectors from the Grok language model combined with numerical features to recommend the best deep learning model for any medical image classification task — without the need to train dozens of models on the target dataset. Key Highlights: Addresses major real-world problems: high computational cost, energy consumption, carbon emissions, and e-waste caused by training large DL models. Built on a newly curated public dataset MedicalRec-Bench II containing 3,500 research papers and over 6,000 model evaluation records across diverse medical imaging tasks. Evaluated under four feature configurations (MedicalRec I) using 13 different models. Achieves strong performance with HitRate@100 ranging from 72.43% to 77.08% — the highest among compared approaches. Uses composite loss functions and regularization techniques for accurate recommendations. Fully eliminates the trial-and-error process of training multiple models, significantly reducing carbon footprint. This is a complete, self-contained research contribution with a novel dataset and a practical, environmentally conscious solution for the medical AI community. Ideal for: Researchers, academic publishers, journals, or institutions looking for high-quality, ready-to-use work in medical image analysis, recommender systems, and green AI. Price: $1,000 USD (one-time transfer of ownership/rights as agreed). Interested? Contact me for the full manuscript, dataset details, or to discuss terms. @Raminmousa1

For Sale: High-Impact Research Paper – MedicalRec / GROKRec Title: MedicalRec Price: $1,000 USD Looking for a ready-to-publish, novel research paper in medical AI and sustainable deep learning? This paper introduces GROKRec, an innovative recommender framework that uses embedding vectors from the Grok language model combined with numerical features to recommend the best deep learning model for any medical image classification task — without the need to train dozens of models on the target dataset. Key Highlights: Addresses major real-world problems: high computational cost, energy consumption, carbon emissions, and e-waste caused by training large DL models. Built on a newly curated public dataset MedicalRec-Bench II containing 3,500 research papers and over 6,000 model evaluation records across diverse medical imaging tasks. Evaluated under four feature configurations (MedicalRec I) using 13 different models. Achieves strong performance with HitRate@100 ranging from 72.43% to 77.08% — the highest among compared approaches. Uses composite loss functions and regularization techniques for accurate recommendations. Fully eliminates the trial-and-error process of training multiple models, significantly reducing carbon footprint. This is a complete, self-contained research contribution with a novel dataset and a practical, environmentally conscious solution for the medical AI community. Ideal for: Researchers, academic publishers, journals, or institutions looking for high-quality, ready-to-use work in medical image analysis, recommender systems, and green AI. Price: $1,000 USD (one-time transfer of ownership/rights as agreed). Interested? Contact me for the full manuscript, dataset details, or to discuss terms. @Raminmousa1

#for_sell @Raminmousa1

❇️ MindDrive: A Vision-Language-Action Model for Autonomous Driving via Online Reinforcement Learning 🔥 Source code: https://github.com/xiaomi-mlab/Minddrive @Machine_learn