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ReCamMaster: Camera-Controlled Generative Rendering from A Single Video
Source code: https://github.com/KlingAIResearch/ReCamMaster
@Machine_learn
Source code: https://github.com/KlingAIResearch/ReCamMaster
@Machine_learn
❤1
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❇️ MindDrive: A Vision-Language-Action Model for Autonomous Driving via Online Reinforcement Learning 🔥
Source code: https://github.com/xiaomi-mlab/Minddrive
@Machine_learn
Source code: https://github.com/xiaomi-mlab/Minddrive
@Machine_learn
❤3
Machine learning books and papers
#for_sell @Raminmousa1
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
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
❤2
Machine learning books and papers pinned «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…»
Uniface
Automate face detection, recognition, and analysis of key facial landmarks with the Uniface Python library.
https://github.com/yakhyo/uniface
@Machine_learn
Automate face detection, recognition, and analysis of key facial landmarks with the Uniface Python library.
https://github.com/yakhyo/uniface
@Machine_learn
🔥2
Understanding Attention
From Q, K, V to Modern Transformer Attention
https://drive.google.com/file/d/1fCHQ5xCQJ6jZszAYf-qP3VIySbzFIEDv/view
@Machine_learn
From Q, K, V to Modern Transformer Attention
https://drive.google.com/file/d/1fCHQ5xCQJ6jZszAYf-qP3VIySbzFIEDv/view
@Machine_learn
❤3
با عرض سلام این مقاله به صورت کامل واگذار میشه به همراه پیاده سازی, مجموعه داده و قالب latex. هزینه کار ۱۰۰۰ دلار
@Raminmousa1
📖 "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
arxiv.org/pdf/2605.29713
#GenerativeAI #Mathematics #DeepLearning #AIResearch #MachineLearning #arXiv
✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk
@Machine_learn
❤2
🔥 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
🔗 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
❤2
🔥 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
🔗 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
❤2
Machine learning books and papers
#for_sell @Raminmousa1
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
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
❤1
Machine learning books and papers pinned «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…»