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

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@Machine_learn

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

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@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

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@Machine_learn

📑 A gentle introduction to pangenomics


📎 Study the paper


@Machine_learn

Machine Learning Systems
Principles and Practices of Engineering Artificially Intelligent Systems

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@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

🔹 Title: TreePO: Bridging the Gap of Policy Optimization and Efficacy and Inference Efficiency with Heuristic Tree-based Modeling

🔹 Publication Date: Published on Aug 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.17445
• PDF: https://arxiv.org/pdf/2508.17445

@Machine_learn

🔹 Title: FastMesh:Efficient Artistic Mesh Generation via Component Decoupling

🔹 Publication Date: Published on Aug 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19188
• PDF: https://arxiv.org/pdf/2508.19188
• Project Page: https://jhkim0759.github.io/projects/FastMesh/

@Machine_learn

🛠️OpenAI just released new guide on how coding agents like GPT-5.1-Codex-Max plug into everyday engineering workflow

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@Machine_learn

با عرض سلام برای مقاله زیر نیاز به نفرات ۲ و ۳ داریم.

KG-Psy: A Knowledge-Graph and GPT-5 Based Framework for Personalized Clinical Decision Support in Bipolar Disorder and Borderline Personality Disorder
 
Abstract: Accurate diagnosis and personalized treatment planning for complex psychiatric disorders such as Bipolar Disorder (BD) and Borderline Personality Disorder (BPD) remain major challenges due to overlapping symptoms, fluctuating mood patterns, and heterogeneous clinical presentations. To address these challenges, we introduce KG-Psy, a hybrid neuro-symbolic framework that combines a domain-specific psychiatric Knowledge Graph (KG) with the advanced reasoning capabilities of GPT-5.
KG-Psy constructs multi-layer psychiatric knowledge graphs encoding symptom trajectories, neural correlates, pharmacological mechanisms, therapeutic guidelines, comorbidities, and behavioral patterns extracted from large-scale clinical literature. GPT-5 is employed to extract clinical entities, infer latent symptom-neural relationships, assess diagnostic likelihoods, and generate patient-specific treatment recommendations. The integration of structured KG reasoning with LLM-based inference allows KG-Psy to produce interpretable, evidence-supported, and clinically actionable outputs.
We evaluated KG-Psy on 310 de-identified psychiatric case reports and 12 expert-validated benchmark scenarios. The framework achieved 91.5% F1-score in distinguishing BD from BPD and an average pathway confidence of 86.9%, indicating robust multi-step inference. In personalized treatment recommendation tasks, KG-Psy achieved 88.7% accuracy, outperforming LLM-only and KG-only baselines by 23% and 31%, respectively.
....
 
Keywords: Bipolar Disorder, Borderline Personality Disorder, Knowledge Graph, GPT-5, Personalized Treatment
 2 :20 milion
3 :15 milion
@Raminmousa
@Machine_learn
@paper4money