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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
با عرض سلام سه موضوع زیر جهت نگارش مقالات مدنظر داریم. که در هر سه مقاله به دو جایگاه نیاز داریم. مقالات کاملا مشارکتی هست و علاوه بر تقبل هزینه کار نیز باید انجام بشه.
1: Survey on knowledge graph and large language models
_ auth2: 300$
_auth3:200$
2: Survey on challenges of large language models
_ auth2: 300$
_auth3:200$
3: New learning model for skin cancer detection
_ auth2: 300$
_auth3:200$
جهت مشارکت میتونین با ایدی بنده در ارتباط باشین. زمان شروع هر مقاله یک هفته بعد از تشکیل تیم.
@Raminmousa1
@Machine_learn
Maths, CS & AI Compendium: A free textbook for aspiring AI/ML engineers
🚀 A large open-source compendium on mathematics, computer science, and AI has gone viral on GitHub. The project already has around 6.3K stars.
📚 The author positions it as a "non-traditional textbook" for practitioners: less dry notation, more intuition, connections between topics, and real-world context.
📖 It contains 20 chapters:
* Vectors, matrices, calculus
* Statistics and probability
* Machine learning and deep learning
* NLP, computer vision, audio/speech
* Multimodal learning and autonomous systems
* GNN, OS, algorithms
* Production engineering, GPU/SIMD
* AI inference, ML systems design, and applied AI
💡 This is a great resource for those who want to not just "learn ML," but to build a solid foundation: mathematics → CS → ML systems → modern AI.
🔗 GitHub: https://github.com/HenryNdubuaku/maths-cs-ai-compendium
@Machine_learn
🔖 Comprehensive Practical Course on Reinforcement Learning
We've found a repository that will help you learn Reinforcement Learning, from basic concepts to advanced algorithms.
The author supports the theory with practical examples using TensorFlow, making the material ideal for self-study.
⛓️ Link to GitHub
https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow
@Machine_learn
🔥 Awesome open-source project to learn more about Transformer Models! 🤖✨
We found this interactive website that shows you visually how transformer models work. 🌐📊
Transformer Explainer:
https://poloclub.github.io/transformer-explainer/
@Machine_learn
Game Theory: http://arxiv.org/abs/1512.06808
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#GameTheory #Gamification #Mathematics #Statistics #Probability
@Machine_learn
Dataset Name: Real / Fake Job Posting Prediction
Basic Description: Dataset of real and fake job postings
📖 FULL DATASET DESCRIPTION:
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This dataset contains 18K job descriptions out of which about 800 are fake. The data consists of both textual information and meta-information about the jobs. The dataset can be used to create classification models which can learn the job descriptions which are fraudulent.
The University of the Aegean | Laboratory of Information & Communication Systems Security http://emscad.samos.aegean.gr/
The dataset is very valuable as it can be used to answer the following questions:
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (17 MB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/shivamb/real-or-fake-fake-jobposting-prediction
📊 Additional information:
==================================
File count not found
Views: 341,000
Downloads: 41,400
@Machine_learn
با عرض سلام نیازمند co-author برای مقاله زیر هستیم
مقاله فقط دوتا نویسنده خواهد داشت.
Title: Multi-Class Alzheimer’s Disease (AD)classification using Vit Transformer andIndependently recurrent neural network(IndRNN)
ABSTRACT: Alzheimer’s disease (AD) is a neurological disorder that is associated with slow andsometimes rapid progression that destroys human thought and consciousness.
Price: 250$
@Raminmousa
@paper4money
@Machine_learn
سلام دوستانی که مقاله ی Transaction می خواستن می تونن در این مقاله مشارکت کنند.
@Raminmousa
🔥 Lift4D: Harmonizing Single-View 3D Estimation for 4D Reconstruction In-the-Wild
💡 The paper presents Lift4D, a test-time optimization framework for reconstructing dynamic non-rigid objects from monocular video. The problem addressed is the difficulty in reconstructing 4D representations of dynamic objects from single-view video due to the scarcity of 4D training data and the limitations of prior approaches that either directly predict 4D representations or initialize a 3D representation and refine it based on video evidence.
The method involves adapting a single-view 3D reconstruction model to yield temporally consistent per-frame predictions, which provides a coherent initialization for a deformable 3D Gaussian Splatting representation. This representation is then optimized to match the input video through an occlusion-aware optimization that recovers visible surface details and completes unobserved regions using a view-conditioned diffusion prior.
The results show that Lift4D improves over prior 4D reconstruction methods, particularly on challenging in-the-wild sequences with severe occlusions and non-rigid motion. The framework effectively handles complex scenarios by integrating visual cues from direct observations with data-driven priors over geometry and appearance, making it a significant contribution to the field of 4D reconstruction from monocular video.
📅 Published on Jun 22
🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.23688
• PDF: https://arxiv.org/pdf/2606.23688
• Project Page: https://lift4d.github.io/
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@Machine_learn
Title: A Multi-Task Framework Unifying Classification and Regression for Microgrid Power (kWh) Forecasting: Modified FEDformer
Abstract:........
Keywords: Microgrid Power forecasting; Transformer; FedFormer; Regression; Classification
Price:
2: 500$
3: 400$
Journal: IEEE Power & Energy Society
@Raminmousa
@Paper4money
@Machine_learn
🔥 World Action Models: A Survey
💡 The paper World Action Models A Survey provides a comprehensive overview of World Action Models, which are predictive action systems that generate future states for decision making. These models balance representational richness against computational constraints, and recent developments have led to a blurring of boundaries among various related models. The survey aims to clarify these boundaries and provide a common account of the field.
The authors organize existing works into two complementary views. The first view examines what each method is required to generate, including rendered futures, latent futures, and video generation free action reasoning. The second view decomposes each method into its predictive substrate, backbone, action coupling, and deployment regime. This anatomy allows for a unified discussion of key aspects such as interactability, causality, persistence, physical plausibility, and generalization.
The survey reveals a consistent design pattern in World Action Models, where design choices trade representational richness against compute, memory, latency, and action label cost. The authors find that the field is moving towards methods that generate less of the future while preserving what is required for control. The survey provides a clear and unified account of the field, covering data, evaluation, and open challenges, and provides a foundation for future research in World Action Models.
The main contributions of the paper are to clarify the boundaries and definitions of World Action Models, to provide a comprehensive overview of existing works, and to identify a consistent design pattern in the field. The survey also highlights the key challenges and open issues in World Action Models, including the need for more efficient and effective methods that balance representational richness against computational constraints. Overall, the paper provides a valuable resource for researchers and practitioners in the field of World Action Models, and helps to advance the state of the art in predictive action systems.
📅 Published on Jun 18
🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.20781
• PDF: https://arxiv.org/pdf/2606.20781
• Project Page: https://world-action-models.github.io/
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@Machine_learn
با عرض سلام یکی از مقالاتمون در حوزه ی wound image classification در ژورنال nature scientific reports ریوایزد خورده و جایگاه های ۲ و ۵ اش قابل اضافه شدن می باشد. دوستانی که نیاز دارن می تونن جهت ثبت اسم به ایدی بنده پیام بدن
Price
2: 300$
5:150$
@Raminmousa
@Paper4money
@Machine_learn
با عرض سلام مقاله MedicalRec توسط بنده و دوستان ارائه شد. این مقاله جهت ارائه ی سیستم پیشنهاد دهنده مدل طبقه بندی برای تصاویر پزشکی میباشد. در ادامه ما می خواهیم MedicalRec2 را توسعه دهیم که یک مدل پیشنهاد دهنده طبقه بند و تقسیم بند در حوزه ی پزشکی می باشد. از این رو نفرات ۲ تا ۶ این مقاله را جهت مشارکت در نظر داریم. هزینه ها از قرار زیر می باشند.
2: 500$
3: 400$
4: 300$
5: 250$
6: 200$
جهت مشارکت با ایدی بنده در ارتباط باشین.
@Raminmousa
@Paper4money
تنها ۳ روز تا سابمیت این مقاله باقی مونده....!
Читать полностью…
با عرض سلام یکی از مقالاتمون در حوزه ی wound image classification در ژورنال nature scientific reports ریوایزد خورده و جایگاه های ۲، ۴ و ۵ اش قابل اضافه شدن می باشد. دوستانی که نیاز دارن می تونن جهت ثبت اسم به ایدی بنده پیام بدن
Price
2: 300$
4: 200$
3:150$
@Raminmousa
@Paper4money
@Machine_learn
🔖 One of the most useful books on Agentic AI
This is not just a textbook, but a comprehensive overview of modern LLMs, model training, RL, inference, quality assessment, and building AI agents.
It's an excellent option to get a holistic picture and understand which topics deserve deeper study.
⛓️ Link to the book
https://arxiv.org/abs/2606.24937
@Machine_learn
10 GitHub repositories that are worth checking out for an AI engineer 🤖
1. Hands-On AI Engineering 🛠️
A collection of AI applications and agent systems with practical use cases of LLM.
👉 https://github.com/Sumanth077/Hands-On-AI-Engineering
2. Hands-On Large Language Models 📘
👉 https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
3. AI Agents for Beginners 🎓
👉 https://github.com/microsoft/ai-agents-for-beginners
4. GenAI Agents 🤖
👉 https://github.com/NirDiamant/GenAI_Agents
5. Made With ML 🚀
👉 https://github.com/GokuMohandas/Made-With-ML
6. Learn Harness Engineering ⚙️
👉 https://github.com/walkinglabs/learn-harness-engineering
7. AutoResearch 🔬
👉 https://github.com/karpathy/autoresearch
8. Designing Machine Learning Systems 📚
👉 https://github.com/chiphuyen/dmls-book
9. Awesome LLM Inference ⚡
👉 https://github.com/xlite-dev/Awesome-LLM-Inference
10. LLM Course 🗺️
👉 https://github.com/mlabonne/llm-course
@Machine_learn
Video-LMM Post-Training: A Deep Dive into Video Reasoning with Large Multimodal Models
Read
@Machine_learn
با عرض سلام اکانت @Raminmousa به دلیل جابجایی در زمان لاگین تلگرام تعلیق شده و در تلاش برای رفع این مشکلیم. دوستانی که باهام پروژه دارن لطفا با این ایدیم در ارتباط باشن
@Raminmousa1
🔥 MemGUI-Agent: An End-to-End Long-Horizon Mobile GUI Agent with Proactive Context Management
💡 The paper introduces MemGUI-Agent, a mobile GUI agent designed to address the limitations of existing agents on long-horizon tasks. Current agents struggle with retaining intermediate facts across many steps and app transitions, leading to unreliable performance. This limitation is attributed to the ReAct-style prompting approach, which passively accumulates per-step records, causing prompt explosion and dilution of critical cross-app facts.
To address this issue, the authors propose MemGUI-Agent, which uses proactive context management through Context-as-Action, or ConAct. ConAct casts context management as first-class actions emitted by the same policy that selects UI actions. This approach maintains three structured context fields: folded action history, folded UI state, and recent step record, preserving critical UI facts while keeping context compact.
The authors also introduce MemGUI-3K, a dataset with 2,956 trajectories and full ConAct annotations for supervised training and offline analysis. Training an 8B model on MemGUI-3K results in MemGUI-8B-SFT, an 8B MemGUI-Agent that achieves the best open-data 8B performance on MemGUI-Bench and generalizes to the out-of-distribution MobileWorld benchmark.
The contributions of the paper are threefold. Firstly, it identifies the limitations of existing mobile GUI agents on long-horizon tasks and attributes them to the ReAct-style prompting approach. Secondly, it proposes MemGUI-Agent with proactive context management through ConAct, which addresses the limitations of existing agents. Finally, it introduces MemGUI-3K, a dataset for supervised training and offline analysis, and demonstrates the effectiveness of MemGUI-8B-SFT, an 8B MemGUI-Agent trained on this dataset. The code, data, and trained models will be released to facilitate further research and development.
📅 Published on Jun 18
🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.19926
• PDF: https://arxiv.org/pdf/2606.19926
• Project Page: https://memgui-agent.github.io/
🤖 Models citing this paper:
• https://huggingface.co/lgy0404/MemGUI-8B-SFT
📊 Datasets citing this paper:
• https://huggingface.co/datasets/lgy0404/MemGUI-3K
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@Machine_learn
BOOM! I Got a 4x AI Speed Improvement!
NEw Paper: AutoMem Turns Memory Management into a Trainable Cognitive Skill, Boosting Long-Horizon Agents 2-4x
arxiv.org/abs/2607.01224
@Machine_learn
Dataset Name: LFW - People (Face Recognition)
Basic Description: The Labeled Faces in the Wild face recognition dataset.
📖 FULL DATASET DESCRIPTION:
==================================
Welcome to Labeled Faces in the Wild, a database of face photographs designed for studying the problem of unconstrained face recognition. The data set contains more than 13,000 images of faces collected from the web. Each face has been labeled with the name of the person pictured. 1680 of the people pictured have two or more distinct photos in the data set. The only constraint on these faces is that they were detected by the Viola-Jones face detector.
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (244 MB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/atulanandjha/lfwpeople
📊 Additional information:
==================================
File count not found
Views: 268,000
Downloads: 47,300
@Machine_learn
CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation
https://arxiv.org/abs/2602.24286
@LLM_learning
با عرض سلام این مقاله فقط ۳ نویسنده خواهد داشت و زمان تقریبی سابمیت ۲ هفته خواهد بود...!
@Raminmousa
⚡️Lumine: An Open Recipe for Building Generalist Agents in 3D Open Worlds
HF: https://huggingface.co/papers/2511.08892
Peoject: https://www.lumine-ai.org/
Paper: https://arxiv.org/abs/2511.08892
@Machine_learn
🔥 Efficient Guided Generation for Large Language Models
💡 The paper presents an efficient method for guiding large language model text generation using regular expressions and context-free grammars. The problem addressed is that guided generation can be impractical due to significant overhead. The authors propose an approach that adds minimal overhead to the token sequence generation process. This method makes guided generation feasible in practice. The approach is implemented in the open source Python library Outlines, providing a practical solution for efficient guided generation. The results indicate that the method is effective, allowing for guided generation with little to no overhead, which is a significant contribution to the field of natural language processing.
📅 Published on Jul 19, 2023
🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2307.09702
• PDF: https://arxiv.org/pdf/2307.09702
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@Machine_learn
📃 Current Bioinformatics Tools in Precision Oncology
📎 Study paper
@Machine_learn
🎬 ساخت ویدیو
• Sora
• Kling
• Veo
• Seedance
• Lumalabs
🎨 ساخت تصویر
• Google Flow
• Qwen Image
• NanoBanana
• ChatGPT Image
• Grok
🎤 تقلید صدا
• ElevenLabs
• Fish Audio
• Minimax
• Descript
• Respeecher
🧠 تحقیق و کاوش
• ChatGPT
• Gemini
• Perplexity
• NotebookLM
• Deepseek
🗣 ساخت کاراکتر سخنگو
• Heygen
• Synthesia
• D-ID
• Hedra
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🔗 لینک ابزارها:
• ChatGPT → https://chatgpt.com
• Gemini → https://gemini.google.com
• Perplexity → https://perplexity.ai
• Deepseek → https://deepseek.com
• NotebookLM → https://notebooklm.google.com
• Kling → https://klingai.com
• Veo → https://deepmind.google/technologies/veo
• Lumalabs → https://lumalabs.ai
• Sora → https://openai.com/sora
• ElevenLabs → https://elevenlabs.io
• Fish Audio → https://fish.audio
• Descript → https://descript.com
• Heygen → https://heygen.com
• Synthesia → https://synthesia.io
• D-ID → https://d-id.com
/channel/Machine_learn
با عرض سلام یکی از مقالاتمون در حوزه ی wound image classification در ژورنال nature scientific reports ریوایزد خورده و جایگاه های ۲، ۴ و ۵ اش قابل اضافه شدن می باشد. دوستانی که نیاز دارن می تونن جهت ثبت اسم به ایدی بنده پیام بدن
Price
2: 300$
4: 200$
5:150$
@Raminmousa
@Paper4money
@Machine_learn
Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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