Machine learning books and papers

دوستانی که می خوان تو حوزه ی LLM مقاله داشته باشن می تونن تو این مقاله شرکت کنند.

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

"Competitive Programming in Python" This 267-pages book from Cambridge University will teach you 128 Algorithms. Don't miss. 📚 Read @Machine_learn

با عرض سلام مقاله زیر جهت ثبت اسم اماده ی ارسال Title: Recurrent Neural Networks Basic deficiencies: NP-complet feature order Abstract: The problem of time series prediction analyzes patterns in past data to predict the future. Traditional machine learning algorithms, despite achieving impressive results, require manual feature selection. Automatic feature selection along with the addition of time concept in deep recurrent networks has led to the provision of more suitable solutions. The selection of feature order in deep recurrent networks leads to the provision of different results due to the use of Back-propagation. The problem of selecting feature order is an NP-complete problem. In this research, the aim is to provide a solution to improve this problem. .... Price: 2: 500$ 3:400$ 4:300$ 5:200$ @Raminmousa @Machine_learn @paper4money

📄 Deep learning methods for protein representation and function prediction: A comprehensive overview 📎 Study the paper @Machine_learn

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با عرض سلام مقاله زیر جهت ثبت اسم اماده ی ارسال Title: Recurrent Neural Networks Basic deficiencies: NP-complet feature order Abstract: The problem of time series prediction analyzes patterns in past data to predict the future. Traditional machine learning algorithms, despite achieving impressive results, require manual feature selection. Automatic feature selection along with the addition of time concept in deep recurrent networks has led to the provision of more suitable solutions. The selection of feature order in deep recurrent networks leads to the provision of different results due to the use of Back-propagation. The problem of selecting feature order is an NP-complete problem. In this research, the aim is to provide a solution to improve this problem. .... Price: 2: 500$ 3:400$ 4:300$ 5:200$ @Raminmousa @Machine_learn @paper4money

🔹 Title: Autoregressive Universal Video Segmentation Model 🔹 Publication Date: Published on Aug 26 🔹 Paper Links: • arXiv Page: https://arxiv.org/abs/2508.19242 • PDF: https://arxiv.org/pdf/2508.19242 @Machine_learn

📹 Unlock Discovery with AI-Powered Genomics 💥 From Oracle 🎞 Watch @Machine_learn

با عرض سلام اين مقاله دو جايگاه باقي مونده....!

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

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

ایران‌GPU تنها و اولین شرکت بورسی هوش مصنوعی ایران با بیش از ۵ سال سابقه فعالیت حرفه‌ای و با پشتوانه‌ی بیش از ۲۰ دیتاسنتر فعال در سراسر ایران 🌐 ⚡ قدرت پردازش، پایداری و مقیاس‌پذیری واقعی برای تیم‌ها، پژوهشگران و سازمان‌های حرفه‌ای AI 🤖 💡 شروعی مقرون‌به‌صرفه برای پروژه‌های هوش مصنوعی شما 📩 همین حالا درخواست مشاوره را ثبت کنید! https://B2n.ir/qz9613

🔹 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

🔹 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: 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

Machine Learning Systems Principles and Practices of Engineering Artificially Intelligent Systems 📚 Read @Machine_learn

سلام دوستاني كه نياز به اين مقاله دارند امكان واگذاري كامل و يا نفر اول هم امكان پذير.... @Raminmousa