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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
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
📚 Introduction to Linux for Bioinformatics
💥Booklet
🌐 Study
@Machine_learn
📃 A Survey of Deep Learning Methods in Protein Bioinformatics and its Impact on Protein Design
📎 Study the paper
@Machine_learn
Solar Power Generation Forecasting in Europe: A Time Series Analysis Python Code
@Machine_learn
Magic of open source is taking over the Video LoRA space✨
just dropped👇🔥
🍬LTX video community LoRA trainer with I2V support
🍬LTX video Cakify LoRA
🍬LTX video Squish LoRA
(🧨diffusers & comfy workflow)
trainer: https://github.com/Lightricks/LTX-Video-Trainer
LoRA: https://huggingface.co/Lightricks/LTX-Video-Cakeify-LoRA
LoRA2 : https://huggingface.co/Lightricks/LTX-Video-Squish-LoRA
🔥
@Machine_learn
Greetings.
As part of our research, we want to write a review article in the field of pathology. Friends who are interested in the 2nd and 3rd places on this topic can participate.
✅ Approximate start time: April 10th.
Journal: scientific reports https://www.nature.com/srep/
Price:
2: $400
3: $300
I will help with complete explanations and how to write each section.
@Raminmousa
@Machine_learn
@Paper4money
🔥 Transformers Laid Out
📌 Guide
@Machine_learn
Graph Theory and Additive Combinatorics
Exploring Structure and Randomness
📚 link
@Machine_learn
با عرض سلام
در ادامه ی کار تحقیقاتی یک مقاله مروری در حوزه پاتولوژی رو می خواهیم بنویسیم. دوستانی که مایل هستن نفرات ۲ و ٣ این موضوع رو می تونن شرکت کنن.
✅زمان تقریبی شروع ۲۰ فروردین.
Journal: scientific reports https://www.nature.com/srep/
Price:
2: 400$
3: 300$
توضیحات کامل و نحوه نگارش هر بخش رو خودم کمک میکنم.
@Raminmousa
@Machine_learn
@Paper4money
Applied Generative AI for Beginners
@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: 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
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
📃 A Comprehensive Guide to Validating Bioinformatics Findings: From In Silico to In Vitro
📎 Study the 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 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
با عرض سلام
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 تقریبی شروع ۲۰ فروردین.
Journal: scientific reports https://www.nature.com/srep/
برخي از دوستان تخفيف مي خواستن جهت مشاركت كه هزينه ها با تخفيف به شرح زير مي باشد.
🔥🔥🔥🔥
Price:
2: ٢٥ ميلوين
3: ٢٠ ميليون
توضیحات کامل و نحوه نگارش هر بخش رو خودم کمک میکنم.
@Raminmousa
@Machine_learn
@Paper4money
📃 Deep learning in microbiome analysis: a comprehensive review of neural network models
📎 Study the 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 و ٣ 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$
توضیحات کامل و نحوه نگارش هر بخش رو خودم کمک میکنم.
@Raminmousa
@Machine_learn
@Paper4money
FastCuRL: Curriculum Reinforcement Learning with Progressive Context Extension for Efficient Training R1-like Reasoning Models
🖥 Github: https://github.com/nick7nlp/FastCuRL
📕 Paper: https://arxiv.org/abs/2503.17287v1
🌟 Tasks: https://paperswithcode.com/task/language-modeling
@Machine_learn
Bias-Variance Trade-Off in Statistics at MIT OpenCourseWare
📚 Book
@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 و ٣ 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$
توضیحات کامل و نحوه نگارش هر بخش رو خودم کمک میکنم.
@Raminmousa
@Machine_learn
@Paper4money
Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement
🖥 Github: https://github.com/dvlab-research/Seg-Zero
📕 Paper: https://arxiv.org/abs/2503.06520v1
🌟 Dataset: https://paperswithcode.com/dataset/refcoco
📌 Model: https://huggingface.co/Ricky06662/Seg-Zero-7B
@Machine_learn
/channel/addlist/8_rRW2scgfRhOTc0
Читать полностью…
تنها نفر ۲ و ۳ از این باقی موندن....!
Читать полностью…
Introduction to Graph Neural Networks: A Starting Point for Machine Learning Engineers
📓 Paper
@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