Admin: @Raminmousa1
ID: @Machine_learn
link: https://t.me/Machine_learn
Post #3558
3.41K
- 👍 2
- 🔥 2
- 🥰 1
Channel Public Channel
MA Machine learning books and papers
@machine_learn
- Subscribers
- 24.2K
- Photos
- 1.1K
- Videos
- 60
- Links
- 1.5K
Showing posts older than #3561 · Back to latest
Older Posts 20 shown
Post #3557
3.73K
Machine learning books and papers pinned Deleted message
Post #3554
3.42K
Post #3553
3.39K
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
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
- 👍 3
- 🔥 2
- ❤ 1
Post #3552
3.09K
Machine learning books and papers 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:…
با عرض سلام فقط نفر ۲ از این پروژه باقی مانده است....!
@Raminmousa
@Raminmousa
- 👍 2
Post #3551
3.42K
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
Nature Scientific Reports Scientific Reports publishes original research in all areas of the natural and clinical sciences. We believe that if your research is scientifically valid and ... 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
Post #3550
3.01K
📄Multimodal deep learning approaches for precision oncology: a comprehensive review
📎 Study the paper
@Machine_learn
📎 Study the paper
@Machine_learn
- ❤ 2
Post #3549
2.84K
Machine learning books and papers pinned Deleted message
Post #3545
3.05K
Post #3544
2.88K
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
🖥 Github: https://github.com/devoallen/awesome-reasoning-economy-papers
📕 Paper: https://arxiv.org/abs/2503.24377v1
@Machine_learn
- ❤ 2
Post #3543
2.84K
Post #3541
3.14K
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
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
- ❤ 2
- 👍 2
Post #3540
2.85K
📃 A Comprehensive Guide to Validating Bioinformatics Findings: From In Silico to In Vitro
📎 Study the paper
@Machine_learn
📎 Study the paper
@Machine_learn
- 👍 2
Post #3539
3.15K
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
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
- ❤ 3
Post #3538
2.78K
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
Nature Scientific Reports Scientific Reports publishes original research in all areas of the natural and clinical sciences. We believe that if your research is scientifically valid and ... 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
- 👍 2
Post #3537
2.78K
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
🖥 Github: https://github.com/yunncheng/MMRL
📕 Paper: https://arxiv.org/abs/2503.08497v1
🌟 Dataset: https://paperswithcode.com/dataset/imagenet-s
@Machine_learn
- 👍 5
Post #3534
3.46K
Post #3533
2.76K
⚡️ LLM4Decompile
🟡 Github
🟡 Models
🟡 Paper
🟡 Colab
@Machine_learn
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