🔹 Title: OmniHuman-1.5: Instilling an Active Mind in Avatars via Cognitive Simulation

🔹 Publication Date: Published on Aug 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19209
• PDF: https://arxiv.org/pdf/2508.19209
• Project Page: https://omnihuman-lab.github.io/v1_5/

@Machine_learn

🔹 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

📹 AI in Bioinformatics Overcoming Pitfalls in Statistical, ML and Generative AI Approaches

🎞 Watch

@Machine_learn

با عرض سلام برای مقاله ی زیر نفرات ۲ تا ۴ قابل اضافه شدن می باشد.
Title: Independently Recurrent Neural Network XGBoost (IXGBOOST) proposed method for Short term load forecasting

Abstract: Short-term load forecasting (STLF) is one of the most important and critical issue for power system operators. Therefore, it plays a fundamental role in improving the reliability of the power system, facilitating the integration of renewable energy sources and making demand response processes more efficient. Today, electricity forecasting based on sensor data with the increasing popularity of smart meter applications. On the other hand, STLF is one of the most critical inputs for the power plant planning undertaking. STLF reduces the overall scheduling uncertainty added by the intermittent generation of renewable resources. Therefore, it helps to minimize the cost of hydrothermal power generation in a power grid. Machine learning (ML) models have obtained acceptable results in this field. These approaches require manual feature extraction, which is challenging. Because of feature selection, deep learning approaches have automatically achieved results in prediction problems. This research proposes a network approach based on IndRNN+XGBoost to forecast electricity consumption in three modes: hourly, daily and weekly. ....

Journal: Optik

2: 20 milion
3:15 milion
4:10 milion
@Raminmousa
@Machine_learn
@paper4money

📑 A gentle introduction to pangenomics


📎 Study the paper


@Machine_learn

سلام دوستاني كه نياز به اين مقاله دارند امكان واگذاري كامل و يا نفر اول هم امكان پذير....
@Raminmousa