Machine learning books and papers

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با عرض سلام در ادامه ی کار تحقیقاتی یک مقاله مروری در حوزه پاتولوژی رو می خواهیم بنویسیم. دوستانی که مایل هستن نفرات ۲ تا ۵ این موضوع رو می تونن شرکت کنن. ✅زمان تقریبی شروع ۲۰ فروردین. Journal: scientific reports https://www.nature.com/srep/ Price: 2: 400$ 3: 300$ 4: 200$ 5: 150$ توضیحات کامل و نحوه نگارش هر بخش رو خودم کمک میکنم. @Raminmousa @Machine_learn @Paper4money

Introduction to Graph Neural Networks: A Starting Point for Machine Learning Engineers 📓 Paper @Machine_learn

با عرض سلام در ادامه ی کار تحقیقاتی یک مقاله مروری در حوزه پاتولوژی رو می خواهیم بنویسیم. دوستانی که مایل هستن نفرات ۲ تا ۵ این موضوع رو می تونن شرکت کنن. ✅زمان تقریبی شروع ۲۰ فروردین. Journal: Nature ( Modern Phatology) Price: 2: 400$ 3: 300$ 4: 200$ 5: 150$ توضیحات کامل و نحوه نگارش هر بخش رو خودم کمک میکنم. @Raminmousa @Machine_learn @Paper4money

PiEEG kit - bioscience Lab in home for your Brain and Body 🖥 Github: https://github.com/pieeg-club/PiEEG_Kit 📕 Paper: https://arxiv.org/abs/2503.13482 🌟 Methods: https://paperswithcode.com/task/eeg-1 @Machine_learn

Executable Code Actions Elicit Better LLM Agents 1 Feb 2024 · Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, Heng Ji Large Language Model (LLM) agents, capable of performing a broad range of actions, such as invoking tools and controlling robots, show great potential in tackling real-world challenges. LLM agents are typically prompted to produce actions by generating #JSON or text in a pre-defined format, which is usually limited by constrained action space (e.g., the scope of pre-defined tools) and restricted flexibility (e.g., inability to compose multiple tools). This work proposes to use executable Python code to consolidate LLM agents' actions into a unified action space (CodeAct). Integrated with a Python interpreter, CodeAct can execute code actions and dynamically revise prior actions or emit new actions upon new observations through multi-turn interactions. Our extensive analysis of 17 LLMs on API-Bank and a newly curated benchmark shows that CodeAct outperforms widely used alternatives (up to 20% higher success rate). The encouraging performance of CodeAct motivates us to build an open-source #LLM agent that interacts with environments by executing interpretable code and collaborates with users using natural language. To this end, we collect an instruction-tuning dataset CodeActInstruct that consists of 7k multi-turn interactions using CodeAct. We show that it can be used with existing data to improve models in agent-oriented tasks without compromising their general capability. CodeActAgent, finetuned from Llama2 and Mistral, is integrated with #Python interpreter and uniquely tailored to perform sophisticated tasks (e.g., model training) using existing libraries and autonomously self-debug. Paper: https://arxiv.org/pdf/2402.01030v4.pdf Codes: https://github.com/epfllm/megatron-llm https://github.com/xingyaoww/code-act Datasets: MMLU - GSM8K - HumanEval - MATH @Machine_learn

Jointly announcing EAGLE-3 with SGLang: Setting a new record in LLM inference acceleration! - 5x🚀than vanilla (on HF) - 1.4x🚀than EAGLE-2 (on HF) - A record of ~400 TPS on LLama 3.1 8B with a single H100 (on SGLang) - 1.65x🚀in latency even for large bs=64 (on SGLang) - A new scaling law: more training data, better speedup - Apache 2.0 Paper: https://arxiv.org/abs/2503.01840 Code: https://github.com/SafeAILab/EAGLE SGLang version: https://github.com/sgl-project/sglang/pull/4247 @Machine_learn

مقاله ی طبقه بندی زخم چند وجهی که در یکی از بهترین ژورنال های Elsevier به چاپ رسوندیم. Multi-modal wound classification using wound image and location by Swin Transformer and Transformer ✅Accepted ✅ Author: Ramin Mousa, Behnaz Rezaei, Laya Mahmoudi, Jafar Abdollahi If: 7.5 Journal: https://www.sciencedirect.com/journal/expert-systems-with-applications Paper: Link @Machine_learn

مقاله ی طبقه بندی زخم چند وجهی که در یکی از بهترین ژورنال های Elsevier به چاپ رسوندیم. Multi-modal wound classification using wound image and location by Swin Transformer and Transformer ✅Accepted ✅ Author: Ramin Mousa, Behnaz Rezaei, Laya Mahmoudi, Jafar Abdollahi If: 7.5 Journal: https://www.sciencedirect.com/journal/expert-systems-with-applications Link @Machine_learn

📃 Biological Multi-Layer and Single Cell Network-Based Multiomics Models - a Review 📎 Study the paper @Machine_learn

You don't need to buy a GPU for machine learning work! There are other alternatives. Here are some: 1. Google Colab 2. Kaggle 3. Deepnote 4. AWS SageMaker 5. GCP Notebooks 6. Azure Notebooks 7. Cocalc 8. Binder 9. Saturncloud 10. Datablore 11. IBM Notebooks 12. Ola kutrim @Machine_learn

🎁 جشنواره نوروزی آکادمی همراه اول آغاز شد! ❗️ یکبار برای یک سال 🎉 به مناسبت عید نوروز طرح ویژه «اشتراک آموزشی» ارائه شد. با خرید این اشتراک می‌تونی به صورت نامحدود به همه دوره‌های آموزشی دسترسی داشته باشی! و نیازی نیست که برای خرید هر دوره هزینه جداگانه‌ای انجام بدی. برخی از مزایای طرح اشتراک آموزشی: 🔸دسترسی نامحدود به ۲۰۰۰ ساعت محتوای آموزشی متنوع 🔸 بهره‌گیری از برترین اساتید صنعت و دانشگاه 🔸 آموزش‌های کاربردی شغل محور 🔸گواهینامه معتبر آموزشی ⚠️مهلت خرید: فقط تا ۱۵ فروردین ۱۴۰۴ 🚫(ظرفیت محدود) 🌐 جهت ثبت‌نام و مشاهده طرح‌های پیشنهادی به لینک زیر مراجعه کنید. https://link.hamrah.academy/yob @hamrah_academy | آکادمی همراه

LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RL 10 Mar 2025 · Yingzhe Peng, Gongrui Zhang, Miaosen Zhang, Zhiyuan You, Jie Liu, Qipeng Zhu, Kai Yang, Xingzhong Xu, Xin Geng, Xu Yang Enhancing reasoning in Large Multimodal Models (#LMMs) faces unique challenges from the complex interplay between visual perception and logical reasoning, particularly in compact 3B-parameter architectures where architectural constraints limit reasoning capacity and modality alignment. While rule-based reinforcement learning (RL) excels in text-only domains, its multimodal extension confronts two critical barriers: (1) data limitations due to ambiguous answers and scarce complex reasoning examples, and (2) degraded foundational reasoning induced by multimodal pretraining. To address these challenges, we propose \textbf{\method}, a two-stage framework adapting rule-based RL for multimodal reasoning through \textbf{Foundational Reasoning Enhancement (FRE)} followed by \textbf{Multimodal Generalization Training (MGT)}. The FRE stage first strengthens reasoning abilities using text-only data with rule-based RL, then the MGT stage generalizes these reasoning capabilities to multimodal domains. Experiments on Qwen2.5-VL-Instruct-3B demonstrate that \method achieves 4.83\% and 4.5\% average improvements over baselines in multimodal and text-only benchmarks, respectively, with a 3.63\% gain in complex Football Game tasks. These results validate that text-based reasoning enhancement enables effective multimodal generalization, offering a data-efficient paradigm that bypasses costly high-quality multimodal training data. Paper: https://arxiv.org/pdf/2503.07536v1.pdf code: https://github.com/tidedra/lmm-r1 @Machine_learn

This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣  Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/codeprogrammer

با عرض سلام براي مقاله بالا نياز به co-author (نفر اول) هستيم. مجله پيشنهادي جهت سابميت. https://www.springerprofessional.de/financial-innovation/50101254 If6️⃣. 5 🔺🔺🔺🔸🔸🔸🔺🔺🔺 جهت ثبت اسم با ايدي بنده در ارتباط باشين @Raminmousa @Machine_learn @paper4money

VideoPainter: Any-length Video Inpainting and Editing with Plug-and-Play Context Control 7 Mar 2025 · Yuxuan Bian, Zhaoyang Zhang, Xuan Ju, Mingdeng Cao, Liangbin Xie, Ying Shan, Qiang Xu · Video inpainting, which aims to restore corrupted video content, has experienced substantial progress. Despite these advances, existing methods, whether propagating unmasked region pixels through optical flow and receptive field priors, or extending image-inpainting models temporally, face challenges in generating fully masked objects or balancing the competing objectives of background context preservation and foreground generation in one model, respectively. To address these limitations, we propose a novel dual-stream paradigm VideoPainter that incorporates an efficient context encoder (comprising only 6% of the backbone parameters) to process masked videos and inject backbone-aware background contextual cues to any pre-trained video DiT, producing semantically consistent content in a plug-and-play manner. This architectural separation significantly reduces the model's learning complexity while enabling nuanced integration of crucial background context. We also introduce a novel target region ID resampling technique that enables any-length video inpainting, greatly enhancing our practical applicability. Additionally, we establish a scalable dataset pipeline leveraging current vision understanding models, contributing VPData and VPBench to facilitate segmentation-based inpainting training and assessment, the largest video inpainting dataset and benchmark to date with over 390K diverse clips. Using inpainting as a pipeline basis, we also explore downstream applications including video editing and video editing pair data generation, demonstrating competitive performance and significant practical potential. Extensive experiments demonstrate VideoPainter's superior performance in both any-length video inpainting and editing, across eight key metrics, including video quality, mask region preservation, and textual coherence. Paper: https://arxiv.org/pdf/2503.05639v2.pdf Code: https://github.com/TencentARC/VideoPainter Datasets: VPData - VPBench @Machine_learn