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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
با عرض سلام
نفر سوم از مقاله زیر رو جهت همکاری نیاز داریم. این مقاله ۸ ماه داریم روش کار میکنیم.
Title: Gaussian Mixture latent for Recurrent Neural Networks Basic deficiencies
The problem of time series prediction analyzes patterns in past data to predict the future. Traditional machine learning algorithms, despite achieving impressive results, require manual feature selection. Automatic feature selection along with the addition of time concept in deep recurrent networks has led to the provision of more suitable solutions. The selection of feature order in deep recurrent networks leads to the provision of different results due to the use of Back-propagation. The problem of selecting feature order is an NP-complete problem. In this research, the aim is to provide a solution to improve this problem.......!
هزینه مشارکت نفر سوم این مقاله ۱۰۰۰$ و ژورنال هدف Expert system میباشد.
@Raminmousa
@Machine_learn
Microsoft just updated their blog with 300 examples of real-world AI use cases.
📕 Article
@Machine_learn
Practical Statistics for Data Scientists
50+ Essential Concepts Using R and Python
#Python #Book
@Machine_learn
Demystifying Long Chain-of-Thought Reasoning in LLMs
🖥 paper
🧠 code
@Machine_learn
Awesome-LLM-as-a-judge Survey
▪ Github
🔸/channel/deep_learning_proj
RIGNO: A Graph-based framework for robust and accurate operator learning for PDEs on arbitrary domains
Paper: https://arxiv.org/pdf/2501.19205v1.pdf
Code: https://github.com/camlab-ethz/rigno
@Machine_learn
A Little Bit of Reinforcement Learning
from Human Feedback
📓 Book
@Machine_learn
20% از کارهای این مقاله انجام شده و این اخرین فرصت برای جوین شدن به پروژست.
@Raminmousa
با عرض سلام نفر ٥ ام از پروژه جديدمون باقي مونده و ٦ جايگاه ديگه پر شدن.
امكان اموزش كامل كار
كدنويسي كار
نحوه جمع اوري داده ها
نگارش مقاله در اين كار وجود داره
Project Title: MedRec: Medical recommender system for image classification without retraining
Github: https://github.com/Ramin1Mousa/MedicalRec
Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence
Impact factor: 20.8
🔺 5- 300$
جهت مشارکت می تونید به ایدی بنده پیام بدین.
@Raminmousa
WILDCHAT-50M: A Deep Dive Into the Role of Synthetic Data in Post-Training
🖥 Github: https://github.com/penfever/wildchat-50m
📕 Paper: https://arxiv.org/abs/2501.18511v1
🧠 Dataset: https://huggingface.co/collections/nyu-dice-lab/wildchat-50m-679a5df2c5967db8ab341ab7
@Machine_learn
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
Paper: https://arxiv.org/pdf/2401.02954v1.pdf
Code: https://github.com/deepseek-ai/deepseek-llm
Dataset: AlignBench
@Machine_learn
اخرین زمان برای مشارکت در این پروژه تا اخر شب...!
@Raminmousa
با عرض سلام در يكي از پروژه هاي طبقه بندي سرطان پوست نياز به مشاركت داريم. جايگاه نفر سوم خالي مي باشد.
🔸🔻🔸🔻🔸🔻🔻
@Raminmousa
International AI Safety Report
📚 Report
@Machine_learn
How to run 🐋 DeepSeek locally on your Computer
@Machine_learn
DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding
13 Dec 2024 · Zhiyu Wu, Xiaokang Chen, Zizheng Pan, Xingchao Liu, Wen Liu, Damai Dai, Huazuo Gao, Yiyang Ma, Chengyue Wu, Bingxuan Wang, Zhenda Xie, Yu Wu, Kai Hu, Jiawei Wang, Yaofeng Sun, Yukun Li, Yishi Piao, Kang Guan, Aixin Liu, Xin Xie, Yuxiang You, Kai Dong, Xingkai Yu, Haowei Zhang, Liang Zhao, Yisong Wang, Chong Ruan ·
We present DeepSeek-VL2, an advanced series of large Mixture-of-Experts (MoE) Vision-Language Models that significantly improves upon its predecessor, DeepSeek-VL, through two key major upgrades. For the vision component, we incorporate a dynamic tiling vision encoding strategy designed for processing high-resolution images with different aspect ratios. For the language component, we leverage #DeepSeekMoE models with the Multi-head Latent Attention mechanism, which compresses Key-Value cache into latent vectors, to enable efficient inference and high throughput. Trained on an improved vision-language dataset, DeepSeek-VL2 demonstrates superior capabilities across various tasks, including but not limited to visual question answering, optical character recognition, document/table/chart understanding, and visual grounding. Our model series is composed of three variants: DeepSeek-VL2-Tiny, #DeepSeek-VL2-Small and DeepSeek-VL2, with 1.0B, 2.8B and 4.5B activated parameters respectively. DeepSeek-VL2 achieves competitive or state-of-the-art performance with similar or fewer activated parameters compared to existing open-source dense and MoE-based models. Codes and pre-trained models are publicly accessible at https://github.com/deepseek-ai/DeepSeek-VL2.
Paper: https://arxiv.org/pdf/2412.10302v1.pdf
Code: https://github.com/deepseek-ai/deepseek-vl2
Datasets: RefCOCO TextVQA MMBench
DocVQA
💠
/channel/deep_learning_proj
با عرض سلام
تخفیف ۵۰٪ دو پکیچ یادگیری ماشین و یادگیری عمیق که شامل ۳۶ پروژه عملی در بحث پردازش تصویر و پردازش متن می باشند رو در نظر گرفتیم. دوستانی که نیاز به این دو پک دارند می تونن به بنده پیام بدن. ۱ ماه مشاوره ریکان راجع به این پروژه ها هم خواهیم داشت.
🟥🟥🟥🟥🟥🟥
@Raminmousa
Introduction to Python for Computational Science and Engineering
📚 book
@Machine_learn
CoSTI: Consistency Models for (a faster) Spatio-Temporal Imputation
31 Jan 2025 · Javier Solís-García, Belén Vega-Márquez, Juan A. Nepomuceno, Isabel A. Nepomuceno-Chamorro ·
Multivariate Time Series Imputation (MTSI) is crucial for many applications, such as healthcare monitoring and traffic management, where incomplete data can compromise decision-making. Existing state-of-the-art methods, like Denoising Diffusion Probabilistic Models (DDPMs), achieve high imputation accuracy; however, they suffer from significant computational costs and are notably time-consuming due to their iterative nature. In this work, we propose CoSTI, an innovative adaptation of Consistency Models (CMs) for the MTSI domain. CoSTI employs Consistency Training to achieve comparable imputation quality to DDPMs while drastically reducing inference times, making it more suitable for real-time applications. We evaluate CoSTI across multiple datasets and missing data scenarios, demonstrating up to a 98% reduction in imputation time with performance on par with diffusion-based models. This work bridges the gap between efficiency and accuracy in generative imputation tasks, providing a scalable solution for handling missing data in critical spatio-temporal systems.
Paper: https://arxiv.org/pdf/2501.19364v1.pdf
Code: https://github.com/javiersgjavi/costi
@Machine_learn
نفر ۵ از این پروژه همچنان خالی هست...!
@Raminmousa
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@Machine_learn
LLMs can see and hear without any training
30 Jan 2025 · Kumar Ashutosh, Yossi Gandelsman, Xinlei Chen, Ishan Misra, Rohit Girdhar ·
We present MILS: Multimodal Iterative LLM Solver, a surprisingly simple, training-free approach, to imbue multimodal capabilities into your favorite LLM. Leveraging their innate ability to perform multi-step reasoning, MILS prompts the LLM to generate candidate outputs, each of which are scored and fed back iteratively, eventually generating a solution to the task. This enables various applications that typically require training specialized models on task-specific data. In particular, we establish a new state-of-the-art on emergent zero-shot image, video and audio captioning. MILS seamlessly applies to media generation as well, discovering prompt rewrites to improve text-to-image generation, and even edit prompts for style transfer! Finally, being a gradient-free optimization approach, MILS can invert multimodal embeddings into text, enabling applications like cross-modal arithmetic.
Paper: https://arxiv.org/pdf/2501.18096v1.pdf
Code: https://github.com/facebookresearch/mils
✅
/channel/deep_learning_proj
Machine Learning for High-Risk Applications
@Machine_learn
با عرض سلام در يكي از پروژه هاي طبقه بندي سرطان پوست نياز به مشاركت داريم. جايگاه نفر سوم خالي مي باشد.
🔸🔻🔸🔻🔸🔻🔻
@Raminmousa
📃Can social network analysis contribute to supply chain
management? A systematic literature review and
bibliometric analysis
📎 Study paper
@Machine_learn
OpenAI o3-mini System Card
📚 Reed
@Machine_learn
🐋 DeepClaude git clone https://github.com/getasterisk/deepclaude.git
cd deepclaude
▪ Github
▪Docs
@Machine_learn
Natural Language Processing with Transformers Building Language Applications
with Hugging Face
#Book
@Machine_learn
⭐️ Fast Think-on-Graph: Wider, Deeper and Faster Reasoning of Large Language Model on Knowledge Graph
🖥 Github: https://github.com/dosonleung/fasttog
📕 Paper: https://arxiv.org/abs/2501.14300v1
@Machine_learn