Machine learning books and papers

Dataset Name: Real / Fake Job Posting Prediction
Basic Description: Dataset of real and fake job postings

📖 FULL DATASET DESCRIPTION:
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This dataset contains 18K job descriptions out of which about 800 are fake. The data consists of both textual information and meta-information about the jobs. The dataset can be used to create classification models which can learn the job descriptions which are fraudulent.
The University of the Aegean | Laboratory of Information & Communication Systems Security http://emscad.samos.aegean.gr/
The dataset is very valuable as it can be used to answer the following questions:

📥 DATASET DOWNLOAD INFORMATION
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🔴 Dataset Size: Download dataset as zip (17 MB)

🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/shivamb/real-or-fake-fake-jobposting-prediction

📊 Additional information:
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File count not found
Views: 341,000
Downloads: 41,400

@Machine_learn

BOOM! I Got a 4x AI Speed Improvement!

NEw Paper: AutoMem Turns Memory Management into a Trainable Cognitive Skill, Boosting Long-Horizon Agents 2-4x

arxiv.org/abs/2607.01224

@Machine_learn

Machine learning books and papers pinned Deleted message

Machine learning books and papers pinned Deleted message

Dataset Name: LFW - People (Face Recognition)
Basic Description: The Labeled Faces in the Wild face recognition dataset.

📖 FULL DATASET DESCRIPTION:
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Welcome to Labeled Faces in the Wild, a database of face photographs designed for studying the problem of unconstrained face recognition. The data set contains more than 13,000 images of faces collected from the web. Each face has been labeled with the name of the person pictured. 1680 of the people pictured have two or more distinct photos in the data set. The only constraint on these faces is that they were detected by the Viola-Jones face detector.

📥 DATASET DOWNLOAD INFORMATION
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🔴 Dataset Size: Download dataset as zip (244 MB)

🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/atulanandjha/lfwpeople

📊 Additional information:
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File count not found
Views: 268,000
Downloads: 47,300

@Machine_learn

سلام دوستانی که مقاله ی Transaction می خواستن می تونن در این مقاله مشارکت کنند.
@Raminmousa

CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation

https://arxiv.org/abs/2602.24286

@LLM_learning

🔥 Lift4D: Harmonizing Single-View 3D Estimation for 4D Reconstruction In-the-Wild

💡 The paper presents Lift4D, a test-time optimization framework for reconstructing dynamic non-rigid objects from monocular video. The problem addressed is the difficulty in reconstructing 4D representations of dynamic objects from single-view video due to the scarcity of 4D training data and the limitations of prior approaches that either directly predict 4D representations or initialize a 3D representation and refine it based on video evidence.

The method involves adapting a single-view 3D reconstruction model to yield temporally consistent per-frame predictions, which provides a coherent initialization for a deformable 3D Gaussian Splatting representation. This representation is then optimized to match the input video through an occlusion-aware optimization that recovers visible surface details and completes unobserved regions using a view-conditioned diffusion prior.

The results show that Lift4D improves over prior 4D reconstruction methods, particularly on challenging in-the-wild sequences with severe occlusions and non-rigid motion. The framework effectively handles complex scenarios by integrating visual cues from direct observations with data-driven priors over geometry and appearance, making it a significant contribution to the field of 4D reconstruction from monocular video.

📅 Published on Jun 22

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.23688
• PDF: https://arxiv.org/pdf/2606.23688
• Project Page: https://lift4d.github.io/

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@Machine_learn

با عرض سلام این مقاله فقط ۳ نویسنده خواهد داشت و زمان تقریبی سابمیت ۲ هفته خواهد بود...!

@Raminmousa

Machine learning books and papers pinned a photo

Title: A Multi-Task Framework Unifying Classification and Regression for Microgrid Power (kWh) Forecasting: Modified FEDformer

Abstract:........

Keywords: Microgrid Power forecasting; Transformer; FedFormer; Regression; Classification

Price:
2: 500$
3: 400$

Journal: IEEE Power & Energy Society

@Raminmousa
@Paper4money
@Machine_learn

⚡️Lumine: An Open Recipe for Building Generalist Agents in 3D Open Worlds

HF: https://huggingface.co/papers/2511.08892

Peoject: https://www.lumine-ai.org/

Paper: https://arxiv.org/abs/2511.08892

@Machine_learn

🔥 World Action Models: A Survey

💡 The paper World Action Models A Survey provides a comprehensive overview of World Action Models, which are predictive action systems that generate future states for decision making. These models balance representational richness against computational constraints, and recent developments have led to a blurring of boundaries among various related models. The survey aims to clarify these boundaries and provide a common account of the field.

The authors organize existing works into two complementary views. The first view examines what each method is required to generate, including rendered futures, latent futures, and video generation free action reasoning. The second view decomposes each method into its predictive substrate, backbone, action coupling, and deployment regime. This anatomy allows for a unified discussion of key aspects such as interactability, causality, persistence, physical plausibility, and generalization.

The survey reveals a consistent design pattern in World Action Models, where design choices trade representational richness against compute, memory, latency, and action label cost. The authors find that the field is moving towards methods that generate less of the future while preserving what is required for control. The survey provides a clear and unified account of the field, covering data, evaluation, and open challenges, and provides a foundation for future research in World Action Models.

The main contributions of the paper are to clarify the boundaries and definitions of World Action Models, to provide a comprehensive overview of existing works, and to identify a consistent design pattern in the field. The survey also highlights the key challenges and open issues in World Action Models, including the need for more efficient and effective methods that balance representational richness against computational constraints. Overall, the paper provides a valuable resource for researchers and practitioners in the field of World Action Models, and helps to advance the state of the art in predictive action systems.

📅 Published on Jun 18

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.20781
• PDF: https://arxiv.org/pdf/2606.20781
• Project Page: https://world-action-models.github.io/

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@Machine_learn

🔥 Efficient Guided Generation for Large Language Models

💡 The paper presents an efficient method for guiding large language model text generation using regular expressions and context-free grammars. The problem addressed is that guided generation can be impractical due to significant overhead. The authors propose an approach that adds minimal overhead to the token sequence generation process. This method makes guided generation feasible in practice. The approach is implemented in the open source Python library Outlines, providing a practical solution for efficient guided generation. The results indicate that the method is effective, allowing for guided generation with little to no overhead, which is a significant contribution to the field of natural language processing.

📅 Published on Jul 19, 2023

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2307.09702
• PDF: https://arxiv.org/pdf/2307.09702

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@Machine_learn

با عرض سلام یکی از مقالاتمون در حوزه ی wound image classification در ژورنال nature scientific reports ریوایزد خورده و جایگاه های ۲ و ۵ اش قابل اضافه شدن می باشد. دوستانی که نیاز دارن می تونن جهت ثبت اسم به ایدی بنده پیام بدن
Price
2: 300$
5:150$
@Raminmousa
@Paper4money
@Machine_learn

📃 Current Bioinformatics Tools in Precision Oncology


📎 Study paper

@Machine_learn

Machine learning books and papers pinned a photo

با عرض سلام مقاله MedicalRec توسط بنده و دوستان ارائه شد. این مقاله جهت ارائه ی سیستم پیشنهاد دهنده مدل طبقه بندی برای تصاویر پزشکی میباشد. در ادامه ما می خواهیم  MedicalRec2  را توسعه دهیم که یک مدل پیشنهاد دهنده طبقه بند و تقسیم بند در حوزه ی پزشکی می باشد. از این رو نفرات ۲ تا ۶ این مقاله را جهت مشارکت در نظر داریم. هزینه ها از قرار زیر می باشند.
2: 500$
3: 400$
4: 300$
5: 250$
6: 200$
جهت مشارکت با ایدی بنده در ارتباط باشین.
@Raminmousa
@Paper4money

🎬 ساخت ویدیو
• Sora
• Kling
• Veo
• Seedance
• Lumalabs

🎨 ساخت تصویر
• Google Flow
• Qwen Image
• NanoBanana
• ChatGPT Image
• Grok

🎤 تقلید صدا
• ElevenLabs
• Fish Audio
• Minimax
• Descript
• Respeecher

🧠 تحقیق و کاوش
• ChatGPT
• Gemini
• Perplexity
• NotebookLM
• Deepseek

🗣 ساخت کاراکتر سخنگو
• Heygen
• Synthesia
• D-ID
• Hedra

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🔗 لینک ابزارها:

• ChatGPT → https://chatgpt.com
• Gemini → https://gemini.google.com
• Perplexity → https://perplexity.ai
• Deepseek → https://deepseek.com
• NotebookLM → https://notebooklm.google.com

• Kling → https://klingai.com
• Veo → https://deepmind.google/technologies/veo
• Lumalabs → https://lumalabs.ai
• Sora → https://openai.com/sora

• ElevenLabs → https://elevenlabs.io
• Fish Audio → https://fish.audio
• Descript → https://descript.com

• Heygen → https://heygen.com
• Synthesia → https://synthesia.io
• D-ID → https://d-id.com


https://t.me/Machine_learn