🔹 Title: OmniHuman-1.5: Instilling an Active Mind in Avatars via Cognitive Simulation

🔹 Publication Date: Published on Aug 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19209
• PDF: https://arxiv.org/pdf/2508.19209
• Project Page: https://omnihuman-lab.github.io/v1_5/

@Machine_learn

دوستانی که نیاز به این مقاله دارن جایگاه ۲، ۳، ۴ خالی می باشد...!
@Raminmousa

gpt-engineer

gpt-engineer is a project in which you specify what you want in plain English and it iterates to produce a working codebase or scaffolded app. It’s an excellent playground for anyone exploring code-generation agents. And the repo contains clear install/usage instructions and a low-friction dev loop.

Creator:   AntonOsika
Stars ⭐️:  55,000
Forked by:  7,300

Github Repo:
https://github.com/AntonOsika/gpt-engineer

@Machine_learn

A combined UNet++ and LSTM approach for breast ultrasound image segmentation

Author: @Raminmousa
Doi:https://kwnsfk27.r.eu-west-1.awstrack.me/L0/https:%2F%2Fdoi.org%2F10.1016%2Fj.fraope.2025.100385/1/0102019a29ac6163-f3d94222-1d67-4a4b-8bb0-22ed875a5711-000000/VLVCKG-PEYhELOu00YPCeqOuaaA=449
Link:https://www.sciencedirect.com/science/article/pii/S2773186325001732?ref=pdf_download&fr=RR-8&rr=9958aaca19dd11fc

@Machine_learn

با عرض سلام در حال تنظیم مقاله ای تحت عنوان
Title: MediSeg: Medical Segmentation and classification Recommender system ....
Journal: IEEE Transactions on Medical Imaging
If: 9.8
این کار ۶ ماه طول خواهد کشید و به مسائل بهینه سازی انرژی، جلوگیری از اموزش مجدد شبکه ها، و مسائل تولید کربن در شبکه ها ی عصبی پرداخته خواهد شد.
هزینه مشارکت :

2: 600$
3:500 $
4: 400$
5:300$
6: 200$
7:200$

@Raminmousa
@Machine_learn
@Paper4money

5-phase path every ML systems engineer follows but almost no one talks about.

📚 Read


@Machine_learn

The Smol Training Playbook:
The Secrets to Building World-Class LLMs

📚 Read

@Machine_learn

با عرض سلام دوستاني كه مايل به اين پروژه هستن مي تونن بهمون ملحق بشن
@Raminmousa

State of AI-assisted Software Development

📕 Report

@Machine_learn

با عرض سلام در حال تنظیم مقاله ای تحت عنوان
Title: MediSeg: Medical Segmentation and classification Recommender system ....
Journal: IEEE Transactions on Medical Imaging
If: 9.8
این کار ۶ ماه طول خواهد کشید و به مسائل بهینه سازی انرژی، جلوگیری از اموزش مجدد شبکه ها، و مسائل تولید کربن در شبکه ها ی عصبی پرداخته خواهد شد.
هزینه مشارکت :

2: 600$
3:500 $
4: 400$
5:300$
6: 200$
7:200$

@Raminmousa
@Machine_learn
@Paper4money

نفر ۷ از این مقاله پر شد...!

🔹 Title: OmniHuman-1.5: Instilling an Active Mind in Avatars via Cognitive Simulation

🔹 Publication Date: Published on Aug 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19209
• PDF: https://arxiv.org/pdf/2508.19209
• Project Page: https://omnihuman-lab.github.io/v1_5/

@Machine_learn

🔹 Title: UltraMemV2: Memory Networks Scaling to 120B Parameters with Superior Long-Context Learning

🔹 Publication Date: Published on Aug 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18756
• PDF: https://arxiv.org/pdf/2508.18756
• Github: https://github.com/ZihaoHuang-notabot/Ultra-Sparse-Memory-Network

@Machine_learn

📹 AI in Bioinformatics Overcoming Pitfalls in Statistical, ML and Generative AI Approaches

🎞 Watch

@Machine_learn

📑 A gentle introduction to pangenomics


📎 Study the paper


@Machine_learn

Machine Learning Systems
Principles and Practices of Engineering Artificially Intelligent Systems

📚 Read

@Machine_learn

🔹 Title: UltraMemV2: Memory Networks Scaling to 120B Parameters with Superior Long-Context Learning

🔹 Publication Date: Published on Aug 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18756
• PDF: https://arxiv.org/pdf/2508.18756
• Github: https://github.com/ZihaoHuang-notabot/Ultra-Sparse-Memory-Network

@Machine_learn

Stochastic and deterministic sampling methods in diffusion models produce noticeably different trajectories, but ultimately both reach the same goal.

Diffusion Explorer allows you to visually compare different sampling methods and training objectives of diffusion models by creating visualizations like the one in the 2 videos.

Additionally, you can, for example, train a model on your own dataset and observe how it gradually converges to a sample from the correct distribution.

Check out this GitHub repository:
https://github.com/helblazer811/Diffusion-Explorer

@Machine_learn