🔹 Title: UltraMemV2: Memory Networks Scaling to 120B Parameters with Superior Long-Context Learning

🔹 Publication Date: Published on Aug 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18756
• PDF: https://arxiv.org/pdf/2508.18756
• Github: https://github.com/ZihaoHuang-notabot/Ultra-Sparse-Memory-Network

@Machine_learn

Stochastic and deterministic sampling methods in diffusion models produce noticeably different trajectories, but ultimately both reach the same goal.

Diffusion Explorer allows you to visually compare different sampling methods and training objectives of diffusion models by creating visualizations like the one in the 2 videos.

Additionally, you can, for example, train a model on your own dataset and observe how it gradually converges to a sample from the correct distribution.

Check out this GitHub repository:
https://github.com/helblazer811/Diffusion-Explorer

@Machine_learn

🔹 Title: TreePO: Bridging the Gap of Policy Optimization and Efficacy and Inference Efficiency with Heuristic Tree-based Modeling

🔹 Publication Date: Published on Aug 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.17445
• PDF: https://arxiv.org/pdf/2508.17445

@Machine_learn

🔹 Title: FastMesh:Efficient Artistic Mesh Generation via Component Decoupling

🔹 Publication Date: Published on Aug 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19188
• PDF: https://arxiv.org/pdf/2508.19188
• Project Page: https://jhkim0759.github.io/projects/FastMesh/

@Machine_learn

🛠️OpenAI just released new guide on how coding agents like GPT-5.1-Codex-Max plug into everyday engineering workflow

📚 Read
@Machine_learn

با عرض سلام اين مقاله دو جايگاه باقي مونده....!

📹 Unlock Discovery with AI-Powered Genomics
💥 From Oracle

🎞 Watch

@Machine_learn

🔹 Title: Autoregressive Universal Video Segmentation Model

🔹 Publication Date: Published on Aug 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19242
• PDF: https://arxiv.org/pdf/2508.19242

@Machine_learn

با عرض سلام مقاله زیر جهت ثبت اسم اماده ی ارسال

Title: Recurrent Neural Networks
Basic deficiencies: NP-complet feature order

Abstract:
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. ....

Price:

2: 500$
3:400$
4:300$
5:200$
@Raminmousa
@Machine_learn
@paper4money

برای این مقاله امکان واگذاری کامل هم وجود داره...!

📄 Deep learning methods for protein representation and function prediction: A comprehensive overview



📎 Study the paper


@Machine_learn

با عرض سلام مقاله زیر جهت ثبت اسم اماده ی ارسال

Title: Recurrent Neural Networks
Basic deficiencies: NP-complet feature order

Abstract:
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. ....

Price:

2: 500$
3:400$
4:300$
5:200$
@Raminmousa
@Machine_learn
@paper4money

"Competitive Programming in Python"

This 267-pages book from Cambridge University will teach you 128 Algorithms. Don't miss.

📚 Read

@Machine_learn

دوستانی که می خوان تو حوزه ی LLM مقاله داشته باشن می تونن تو این مقاله شرکت کنند.