"Competitive Programming in Python"
This 267-pages book from Cambridge University will teach you 128 Algorithms. Don't miss.
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This 267-pages book from Cambridge University will teach you 128 Algorithms. Don't miss.
📚 Read
@Machine_learn
👍1
https://arxiv.org/pdf/2511.22082
Weighted Ensemble Transformer for Identifying Psychiatric Stressors Related to Suicide X (formerly Twitter)
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Weighted Ensemble Transformer for Identifying Psychiatric Stressors Related to Suicide X (formerly Twitter)
@Raminmousa
@Machine_learn
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دوستانی که می خوان تو حوزه ی LLM مقاله داشته باشن می تونن تو این مقاله شرکت کنند.
📑 A comprehensive review of cluster methods for drug–drug interaction network
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📎 Study the paper
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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. ....
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2: 500$
3:400$
4:300$
5:200$
@Raminmousa
@Machine_learn
@paper4money
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
Machine learning books and papers pinned «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…»
دوستان برای این مقاله نیاز به نفرات ۴ و ۵ داریم
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:
4:300$
5:200$
@Raminmousa
@Machine_learn
@paper4money
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:
4:300$
5:200$
@Raminmousa
@Machine_learn
@paper4money
❤2
🔹 Title: ObjFiller-3D: Consistent Multi-view 3D Inpainting via Video Diffusion Models
🔹 Publication Date: Published on Aug 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18271
• PDF: https://arxiv.org/pdf/2508.18271
• Project Page: https://objfiller3d.github.io/
• Github: https://github.com/objfiller3d/ObjFiller-3D
@Machine_learn
🔹 Publication Date: Published on Aug 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18271
• PDF: https://arxiv.org/pdf/2508.18271
• Project Page: https://objfiller3d.github.io/
• Github: https://github.com/objfiller3d/ObjFiller-3D
@Machine_learn
❤4
🔹 Title: ReportBench: Evaluating Deep Research Agents via Academic Survey Tasks
🔹 Publication Date: Published on Aug 14
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.15804
• PDF: https://arxiv.org/pdf/2508.15804
@Machine_learn
🔹 Publication Date: Published on Aug 14
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.15804
• PDF: https://arxiv.org/pdf/2508.15804
@Machine_learn
❤5
🛠️OpenAI just released new guide on how coding agents like GPT-5.1-Codex-Max plug into everyday engineering workflow
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@Machine_learn
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🔹 Title: Forecasting Probability Distributions of Financial Returns with Deep Neural Networks
🔹 Publication Date: Published on Aug 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18921
• PDF: https://arxiv.org/pdf/2508.18921
• Github: https://github.com/jmichankow/deep_learning_probability
@Machine_learn
🔹 Publication Date: Published on Aug 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18921
• PDF: https://arxiv.org/pdf/2508.18921
• Github: https://github.com/jmichankow/deep_learning_probability
@Machine_learn
❤2
Video-LMM Post-Training: A Deep Dive into Video Reasoning with Large Multimodal Models
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@Machine_learn
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