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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
https://arxiv.org/pdf/2504.10452
Integrating Vision and Location with Transformers: A
Multimodal Deep Learning Framework for Medical
Wound Analysis
New Paper
Ramin Mousa
Hadis Taherinia
Khabiba Abdiyeva
Amir Ali Bengari
Mohammadmahdi Vahediahmar
@Machine_learn
Artificial Intelligence Index Report 2025
📚 Report
@Machine_learn
Compute Forecast
📚 Read
@Machine_learn
Multi-modal wound classification using wound image and location by Swin Transformer and Transformer
New paper ✅
کار مشترکی که با دوستان تونستیم چاپش رو بگیریم.
Journal: Expert system with application
If: 7.5
@Machine_learn
📃 Advances and Mechanisms of RNA–Ligand Interaction Predictions
📎 Study the paper
@Machine_learn
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در ادامه ی کار تحقیقاتی یک مقاله مروری در حوزه پاتولوژی رو می خواهیم بنویسیم. دوستانی که مایل هستن نفر۲ این موضوع رو می تونن شرکت کنن.
✅زمان شروع ۲۰ فروردین.
Journal: scientific reports https://www.nature.com/srep/
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توضیحات کامل و نحوه نگارش هر بخش رو خودم کمک میکنم.
@Raminmousa
@Machine_learn
@Paper4money
Llama 3.2 From Scratch
This repository contains a from-scratch, educational PyTorch implementation of Llama 3.2 text models with minimal code dependencies. The implementation is optimized for readability and intended for learning and research purposes.
📌 Guide
@Machine_learn
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در ادامه ی کار تحقیقاتی یک مقاله مروری در حوزه پاتولوژی رو می خواهیم بنویسیم. دوستانی که مایل هستن نفرات ۲ و ٣ این موضوع رو می تونن شرکت کنن.
✅زمان شروع ۲۰ فروردین.
Journal: scientific reports https://www.nature.com/srep/
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2: ٢٥ میلیون
3: ٢٠ ميليون
توضیحات کامل و نحوه نگارش هر بخش رو خودم کمک میکنم.
@Raminmousa
@Machine_learn
@Paper4money
📖 Applied Bioinformatics
💥Free Online Book from Oregon State
🌐 Study
@Machine_learn
Large Language Model Course 😮
📌 Github
@LLM_learning
📽 Current and Future Integrations of Genomics and AI
🎞 Watch
@Raminmousa
LHM: Large Animatable Human Reconstruction Model from a Single Image in Seconds
Animatable 3D human reconstruction from a single image is a challenging problem due to the ambiguity in decoupling geometry, appearance, and deformation. Recent advances in 3D human reconstruction mainly focus on static human modeling, and the reliance of using synthetic 3D scans for training limits their generalization ability. Conversely, optimization-based video methods achieve higher fidelity but demand controlled capture conditions and computationally intensive refinement processes. Motivated by the emergence of large reconstruction models for efficient static reconstruction, we propose LHM (Large Animatable Human Reconstruction Model) to infer high-fidelity avatars represented as 3D Gaussian splatting in a feed-forward pass. Our model leverages a multimodal transformer architecture to effectively encode the human body positional features and image features with attention mechanism, enabling detailed preservation of clothing geometry and texture. To further boost the face identity preservation and fine detail recovery, we propose a head feature pyramid encoding scheme to aggregate multi-scale features of the head regions. Extensive experiments demonstrate that our LHM generates plausible animatable human in seconds without post-processing for face and hands, outperforming existing methods in both reconstruction accuracy and generalization ability.
Paper: https://arxiv.org/pdf/2503.10625v1.pdf
Code: https://github.com/aigc3d/LHM
@Machine_learn
InfiniteYou: Flexible Photo Recrafting While Preserving Your Identity
20 Mar 2025 · Liming Jiang, Qing Yan, Yumin Jia, Zichuan Liu, Hao Kang, Xin Lu ·
Achieving flexible and high-fidelity identity-preserved image generation remains formidable, particularly with advanced Diffusion Transformers (DiTs) like FLUX. We introduce InfiniteYou (InfU), one of the earliest robust frameworks leveraging DiTs for this task. InfU addresses significant issues of existing methods, such as insufficient identity similarity, poor text-image alignment, and low generation quality and aesthetics. Central to InfU is InfuseNet, a component that injects identity features into the DiT base model via residual connections, enhancing identity similarity while maintaining generation capabilities. A multi-stage training strategy, including pretraining and supervised fine-tuning (SFT) with synthetic single-person-multiple-sample (SPMS) data, further improves text-image alignment, ameliorates image quality, and alleviates face copy-pasting. Extensive experiments demonstrate that InfU achieves state-of-the-art performance, surpassing existing baselines. In addition, the plug-and-play design of InfU ensures compatibility with various existing methods, offering a valuable contribution to the broader community.
Paper: https://arxiv.org/pdf/2503.16418v1.pdf
Code: https://github.com/bytedance/infiniteyou
Dataset: 10,000 People - Human Pose Recognition Data
@Machine_learn
Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement
🖥 Github: https://github.com/yunncheng/MMRL
📕 Paper: https://arxiv.org/abs/2503.08497v1
🌟 Dataset: https://paperswithcode.com/dataset/imagenet-s
@Machine_learn
⚡️ LLM4Decompile git clone https://github.com/albertan017/LLM4Decompile.git
cd LLM4Decompile
conda create -n 'llm4decompile' python=3.9 -y
conda activate llm4decompile
pip install -r requirements.txt
🟡 Github
🟡 Models
🟡 Paper
🟡 Colab
@Machine_learn
SeedLM: Compressing LLM Weights into Seeds of Pseudo-Random Generators
📚 Read
@LLM_learning
Large Language Model Agent: A Survey on Methodology, Applications and Challenges
Paper: https://arxiv.org/pdf/2503.21460v1.pdf
Code: https://github.com/luo-junyu/awesome-agent-papers
@Machine_learn
شنبه شروع اين پروژه مي باشد.
دوستاني كه مايل هستند نفر دوم از اين مقاله باقي موند است.
@Raminmousa
شنبه شروع اين پروژه مي باشد.
دوستاني كه مايل هستند نفر دوم از اين مقاله باقي موند است.
@Raminmousa
✔️ "Speech and Language Processing":
🟡Link
@Machine_learn
Carnegie Mellon University's "Advanced Algorithms" course notes
📄 Book
@Machine_learn
با عرض سلام فقط نفر ۲ از این پروژه باقی مانده است....!
@Raminmousa
📄Multimodal deep learning approaches for precision oncology: a comprehensive review
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@Machine_learn
تنها نفر ۳ از این پروژه باقی مانده...!
@Raminmousa
Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models
🖥 Github: https://github.com/devoallen/awesome-reasoning-economy-papers
📕 Paper: https://arxiv.org/abs/2503.24377v1
@Machine_learn
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در ادامه ی کار تحقیقاتی یک مقاله مروری در حوزه پاتولوژی رو می خواهیم بنویسیم. دوستانی که مایل هستن نفرات ۲ و ٣ این موضوع رو می تونن شرکت کنن.
✅زمان شروع ۲۰ فروردین.
Journal: scientific reports https://www.nature.com/srep/
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Price:
2: ٢٥ میلیون
3: ٢٠ ميليون
توضیحات کامل و نحوه نگارش هر بخش رو خودم کمک میکنم.
@Raminmousa
@Machine_learn
@Paper4money
📃 A Comprehensive Guide to Validating Bioinformatics Findings: From In Silico to In Vitro
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در ادامه ی کار تحقیقاتی یک مقاله مروری در حوزه پاتولوژی رو می خواهیم بنویسیم. دوستانی که مایل هستن نفرات ۲ و ٣ این موضوع رو می تونن شرکت کنن.
✅زمان شروع ۲۰ فروردین.
Journal: scientific reports https://www.nature.com/srep/
🔥🔥🔥🔥
Price:
2: ٢٥ میلیون
3: ٢٠ ميليون
توضیحات کامل و نحوه نگارش هر بخش رو خودم کمک میکنم.
@Raminmousa
@Machine_learn
@Paper4money
Mathematics for Computer Science
📚 link
@Machine_learn
عيدكم مُبارك و كُلَّ عامٍ و انتم بالفِ ألفِ خير يارب
اسأل الله أن يعيد عليكم رمضان أعوامًا و أعوام و أن يتقبل مِنا و منكم صالِح الاعمال .🖤
@Machine_learn