✨StreamGaze: Gaze-Guided Temporal Reasoning and Proactive Understanding in Streaming Videos
📝 Summary:
StreamGaze is a new benchmark evaluating how MLLMs use human gaze for temporal and proactive reasoning in streaming videos. It reveals significant performance gaps between current AI models and human abilities in gaze-based temporal reasoning and proactive prediction.
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01707
• PDF: https://arxiv.org/pdf/2512.01707
• Project Page: https://streamgaze.github.io/
• Github: https://github.com/daeunni/StreamGaze
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#StreamGaze #MLLMs #TemporalReasoning #ComputerVision #AI
📝 Summary:
StreamGaze is a new benchmark evaluating how MLLMs use human gaze for temporal and proactive reasoning in streaming videos. It reveals significant performance gaps between current AI models and human abilities in gaze-based temporal reasoning and proactive prediction.
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01707
• PDF: https://arxiv.org/pdf/2512.01707
• Project Page: https://streamgaze.github.io/
• Github: https://github.com/daeunni/StreamGaze
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#StreamGaze #MLLMs #TemporalReasoning #ComputerVision #AI
✨Asking like Socrates: Socrates helps VLMs understand remote sensing images
📝 Summary:
Remote sensing models often show fake reasoning from coarse image understanding. This paper introduces RS-EoT, an iterative, language-driven system with a Socratic multi-agent approach and RL to seek visual evidence. It achieves state-of-the-art results, enabling genuine, evidence-grounded reason...
🔹 Publication Date: Published on Nov 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22396
• PDF: https://arxiv.org/pdf/2511.22396
• Project Page: https://geox-lab.github.io/Asking_like_Socrates/
• Github: https://github.com/GeoX-Lab/Asking_like_Socrates
🔹 Models citing this paper:
• https://huggingface.co/ShaoRun/RS-EoT-7B
✨ Datasets citing this paper:
• https://huggingface.co/datasets/ShaoRun/RS-EoT-4K
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#VLM #RemoteSensing #AI #ReinforcementLearning #MultiAgentSystems
📝 Summary:
Remote sensing models often show fake reasoning from coarse image understanding. This paper introduces RS-EoT, an iterative, language-driven system with a Socratic multi-agent approach and RL to seek visual evidence. It achieves state-of-the-art results, enabling genuine, evidence-grounded reason...
🔹 Publication Date: Published on Nov 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22396
• PDF: https://arxiv.org/pdf/2511.22396
• Project Page: https://geox-lab.github.io/Asking_like_Socrates/
• Github: https://github.com/GeoX-Lab/Asking_like_Socrates
🔹 Models citing this paper:
• https://huggingface.co/ShaoRun/RS-EoT-7B
✨ Datasets citing this paper:
• https://huggingface.co/datasets/ShaoRun/RS-EoT-4K
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#VLM #RemoteSensing #AI #ReinforcementLearning #MultiAgentSystems
✨Accelerating Streaming Video Large Language Models via Hierarchical Token Compression
📝 Summary:
Streaming VideoLLMs face high latency from ViT encoding and LLM pre-filling. STC, a hierarchical framework, optimizes this by caching features and pruning tokens. It reduces latency by up to 24.5 percent for ViT and 45.3 percent for LLM pre-filling, retaining 99 percent accuracy.
🔹 Publication Date: Published on Nov 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.00891
• PDF: https://arxiv.org/pdf/2512.00891
• Github: https://github.com/lern-to-write/STC
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#VideoLLM #LLM #DeepLearning #AI #PerformanceOptimization
📝 Summary:
Streaming VideoLLMs face high latency from ViT encoding and LLM pre-filling. STC, a hierarchical framework, optimizes this by caching features and pruning tokens. It reduces latency by up to 24.5 percent for ViT and 45.3 percent for LLM pre-filling, retaining 99 percent accuracy.
🔹 Publication Date: Published on Nov 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.00891
• PDF: https://arxiv.org/pdf/2512.00891
• Github: https://github.com/lern-to-write/STC
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#VideoLLM #LLM #DeepLearning #AI #PerformanceOptimization
✨Script: Graph-Structured and Query-Conditioned Semantic Token Pruning for Multimodal Large Language Models
📝 Summary:
Script is a new plug-and-play token pruning method for multimodal large language models. It uses graph-structured and query-conditioned modules to remove redundant visual tokens while preserving relevant information without retraining. This boosts efficiency and accuracy, achieving significant sp...
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01949
• PDF: https://arxiv.org/pdf/2512.01949
• Github: https://01yzzyu.github.io/noscript.github.io/
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#MultimodalAI #LLMs #TokenPruning #DeepLearning #Efficiency
📝 Summary:
Script is a new plug-and-play token pruning method for multimodal large language models. It uses graph-structured and query-conditioned modules to remove redundant visual tokens while preserving relevant information without retraining. This boosts efficiency and accuracy, achieving significant sp...
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01949
• PDF: https://arxiv.org/pdf/2512.01949
• Github: https://01yzzyu.github.io/noscript.github.io/
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#MultimodalAI #LLMs #TokenPruning #DeepLearning #Efficiency
✨OpenREAD: Reinforced Open-Ended Reasoing for End-to-End Autonomous Driving with LLM-as-Critic
📝 Summary:
OpenREAD enhances autonomous driving via end-to-end reinforcement fine-tuning for both reasoning and planning. It uses an LLM critic to quantify open-ended reasoning, achieving state-of-the-art performance by addressing prior limitations.
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01830
• PDF: https://arxiv.org/pdf/2512.01830
• Github: https://github.com/wyddmw/OpenREAD
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#AutonomousDriving #LLMs #ReinforcementLearning #AI #Robotics
📝 Summary:
OpenREAD enhances autonomous driving via end-to-end reinforcement fine-tuning for both reasoning and planning. It uses an LLM critic to quantify open-ended reasoning, achieving state-of-the-art performance by addressing prior limitations.
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01830
• PDF: https://arxiv.org/pdf/2512.01830
• Github: https://github.com/wyddmw/OpenREAD
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#AutonomousDriving #LLMs #ReinforcementLearning #AI #Robotics
✨Flash-DMD: Towards High-Fidelity Few-Step Image Generation with Efficient Distillation and Joint Reinforcement Learning
📝 Summary:
Flash-DMD accelerates generative diffusion models via efficient timestep-aware distillation and joint reinforcement learning. This framework achieves faster convergence, high-fidelity few-step generation, and stabilizes RL training using distillation as a regularizer, all with reduced computation...
🔹 Publication Date: Published on Nov 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20549
• PDF: https://arxiv.org/pdf/2511.20549
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#DiffusionModels #ImageGeneration #ReinforcementLearning #ModelDistillation #GenerativeAI
📝 Summary:
Flash-DMD accelerates generative diffusion models via efficient timestep-aware distillation and joint reinforcement learning. This framework achieves faster convergence, high-fidelity few-step generation, and stabilizes RL training using distillation as a regularizer, all with reduced computation...
🔹 Publication Date: Published on Nov 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20549
• PDF: https://arxiv.org/pdf/2511.20549
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#DiffusionModels #ImageGeneration #ReinforcementLearning #ModelDistillation #GenerativeAI
👍1
✨OmniFusion: Simultaneous Multilingual Multimodal Translations via Modular Fusion
📝 Summary:
OmniFusion is a multimodal translation system integrating pretrained foundation models with LLMs via a novel fusion strategy. It enables simultaneous multilingual translation using audio and visual inputs, reducing latency and improving quality over cascaded systems.
🔹 Publication Date: Published on Nov 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.00234
• PDF: https://arxiv.org/pdf/2512.00234
• Github: https://github.com/saikoneru/OmniFusion
🔹 Models citing this paper:
• https://huggingface.co/skoneru/OmniFusion
• https://huggingface.co/skoneru/OmniFusion_v2
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#MultimodalAI #LLMs #MachineTranslation #FoundationModels #AIResearch
📝 Summary:
OmniFusion is a multimodal translation system integrating pretrained foundation models with LLMs via a novel fusion strategy. It enables simultaneous multilingual translation using audio and visual inputs, reducing latency and improving quality over cascaded systems.
🔹 Publication Date: Published on Nov 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.00234
• PDF: https://arxiv.org/pdf/2512.00234
• Github: https://github.com/saikoneru/OmniFusion
🔹 Models citing this paper:
• https://huggingface.co/skoneru/OmniFusion
• https://huggingface.co/skoneru/OmniFusion_v2
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#MultimodalAI #LLMs #MachineTranslation #FoundationModels #AIResearch
👍1
✨Structured Extraction from Business Process Diagrams Using Vision-Language Models
📝 Summary:
This paper presents a method using Vision-Language Models to extract structured JSON from BPMN diagram images. It incorporates OCR for text enrichment, demonstrating improved model performance and enabling extraction when source files are unavailable.
🔹 Publication Date: Published on Nov 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22448
• PDF: https://arxiv.org/pdf/2511.22448
• Github: https://github.com/pritamdeka/BPMN-VLM
✨ Datasets citing this paper:
• https://huggingface.co/datasets/pritamdeka/BPMN-VLM
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#VisionLanguageModels #BPMN #InformationExtraction #AI #ComputerVision
📝 Summary:
This paper presents a method using Vision-Language Models to extract structured JSON from BPMN diagram images. It incorporates OCR for text enrichment, demonstrating improved model performance and enabling extraction when source files are unavailable.
🔹 Publication Date: Published on Nov 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22448
• PDF: https://arxiv.org/pdf/2511.22448
• Github: https://github.com/pritamdeka/BPMN-VLM
✨ Datasets citing this paper:
• https://huggingface.co/datasets/pritamdeka/BPMN-VLM
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#VisionLanguageModels #BPMN #InformationExtraction #AI #ComputerVision
❤1
✨LFM2 Technical Report
📝 Summary:
LFM2 is a family of compact foundation models designed for efficient on-device deployment. It uses hardware-in-the-loop architecture search and advanced training to achieve high performance across diverse tasks, including multimodal applications.
🔹 Publication Date: Published on Nov 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.23404
• PDF: https://arxiv.org/pdf/2511.23404
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#FoundationModels #EdgeAI #MultimodalAI #AIResearch #MachineLearning
📝 Summary:
LFM2 is a family of compact foundation models designed for efficient on-device deployment. It uses hardware-in-the-loop architecture search and advanced training to achieve high performance across diverse tasks, including multimodal applications.
🔹 Publication Date: Published on Nov 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.23404
• PDF: https://arxiv.org/pdf/2511.23404
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#FoundationModels #EdgeAI #MultimodalAI #AIResearch #MachineLearning
✨Learning Eigenstructures of Unstructured Data Manifolds
📝 Summary:
This deep learning framework learns spectral bases directly from unstructured data, eliminating traditional operator selection and eigendecomposition. It provides a data-driven alternative for geometry processing, recovering spectral bases and eigenvalues unsupervised without explicit operator co...
🔹 Publication Date: Published on Nov 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01103
• PDF: https://arxiv.org/pdf/2512.01103
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#DeepLearning #DataScience #ManifoldLearning #GeometryProcessing #UnsupervisedLearning
📝 Summary:
This deep learning framework learns spectral bases directly from unstructured data, eliminating traditional operator selection and eigendecomposition. It provides a data-driven alternative for geometry processing, recovering spectral bases and eigenvalues unsupervised without explicit operator co...
🔹 Publication Date: Published on Nov 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01103
• PDF: https://arxiv.org/pdf/2512.01103
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#DeepLearning #DataScience #ManifoldLearning #GeometryProcessing #UnsupervisedLearning
✨Wikontic: Constructing Wikidata-Aligned, Ontology-Aware Knowledge Graphs with Large Language Models
📝 Summary:
Wikontic is a multi-stage pipeline that builds high-quality, ontology-consistent knowledge graphs from text. It achieves state-of-the-art performance in information retention and efficiency, providing structured grounding for LLMs.
🔹 Publication Date: Published on Nov 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.00590
• PDF: https://arxiv.org/pdf/2512.00590
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#KnowledgeGraphs #LLMs #Ontologies #NLP #AI
📝 Summary:
Wikontic is a multi-stage pipeline that builds high-quality, ontology-consistent knowledge graphs from text. It achieves state-of-the-art performance in information retention and efficiency, providing structured grounding for LLMs.
🔹 Publication Date: Published on Nov 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.00590
• PDF: https://arxiv.org/pdf/2512.00590
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#KnowledgeGraphs #LLMs #Ontologies #NLP #AI
✨SCALE: Selective Resource Allocation for Overcoming Performance Bottlenecks in Mathematical Test-time Scaling
📝 Summary:
SCALE improves LLM math reasoning by selectively allocating resources based on sub-problem difficulty. It addresses uniform allocation bottlenecks, boosting accuracy up to 13.75% and cutting costs by 33-53% compared to uniform scaling.
🔹 Publication Date: Published on Nov 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.00466
• PDF: https://arxiv.org/pdf/2512.00466
• Github: https://github.com/XiaoYang66/DualThinking
✨ Datasets citing this paper:
• https://huggingface.co/datasets/YangXiao-nlp/DualThinking
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#LLM #AI #MachineLearning #PerformanceOptimization #MathReasoning
📝 Summary:
SCALE improves LLM math reasoning by selectively allocating resources based on sub-problem difficulty. It addresses uniform allocation bottlenecks, boosting accuracy up to 13.75% and cutting costs by 33-53% compared to uniform scaling.
🔹 Publication Date: Published on Nov 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.00466
• PDF: https://arxiv.org/pdf/2512.00466
• Github: https://github.com/XiaoYang66/DualThinking
✨ Datasets citing this paper:
• https://huggingface.co/datasets/YangXiao-nlp/DualThinking
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#LLM #AI #MachineLearning #PerformanceOptimization #MathReasoning
✨Agentic Policy Optimization via Instruction-Policy Co-Evolution
📝 Summary:
INSPO introduces a novel framework dynamically optimizing instructions within the reinforcement learning loop for autonomous agents. It substantially outperforms static instruction methods in multi-turn reasoning by discovering innovative, strategic reasoning paths.
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01945
• PDF: https://arxiv.org/pdf/2512.01945
• Github: https://github.com/cambridgeltl/inspo
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#ReinforcementLearning #AIAgents #PolicyOptimization #MachineLearning #AI
📝 Summary:
INSPO introduces a novel framework dynamically optimizing instructions within the reinforcement learning loop for autonomous agents. It substantially outperforms static instruction methods in multi-turn reasoning by discovering innovative, strategic reasoning paths.
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01945
• PDF: https://arxiv.org/pdf/2512.01945
• Github: https://github.com/cambridgeltl/inspo
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#ReinforcementLearning #AIAgents #PolicyOptimization #MachineLearning #AI
✨Where Culture Fades: Revealing the Cultural Gap in Text-to-Image Generation
📝 Summary:
Multilingual text-to-image models often generate culturally neutral images. This paper identifies specific neurons for cultural information and proposes two strategies: inference-time activation and layer-targeted enhancement. These methods improve cultural consistency while preserving image qual...
🔹 Publication Date: Published on Nov 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.17282
• PDF: https://arxiv.org/pdf/2511.17282
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#TextToImage #CulturalAI #ResponsibleAI #DeepLearning #AIResearch
📝 Summary:
Multilingual text-to-image models often generate culturally neutral images. This paper identifies specific neurons for cultural information and proposes two strategies: inference-time activation and layer-targeted enhancement. These methods improve cultural consistency while preserving image qual...
🔹 Publication Date: Published on Nov 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.17282
• PDF: https://arxiv.org/pdf/2511.17282
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#TextToImage #CulturalAI #ResponsibleAI #DeepLearning #AIResearch
✨DreamingComics: A Story Visualization Pipeline via Subject and Layout Customized Generation using Video Models
📝 Summary:
DreamingComics improves story visualization with better layout control, character consistency, and style. It uses a video diffusion-transformer, regional positional encoding, and an LLM for comic-style layouts, significantly boosting visual quality.
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01686
• PDF: https://arxiv.org/pdf/2512.01686
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#StoryVisualization #GenerativeAI #DiffusionModels #LLM #AIArt
📝 Summary:
DreamingComics improves story visualization with better layout control, character consistency, and style. It uses a video diffusion-transformer, regional positional encoding, and an LLM for comic-style layouts, significantly boosting visual quality.
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01686
• PDF: https://arxiv.org/pdf/2512.01686
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#StoryVisualization #GenerativeAI #DiffusionModels #LLM #AIArt
❤1
🚀 Master Data Science & Programming!
Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today!
🔰 Machine Learning with Python
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://news.1rj.ru/str/CodeProgrammer
🔖 Machine Learning
Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.
https://news.1rj.ru/str/DataScienceM
🧠 Code With Python
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills.
https://news.1rj.ru/str/DataScience4
🎯 PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://news.1rj.ru/str/DataScienceQ
💾 Kaggle Data Hub
Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://news.1rj.ru/str/datasets1
🧑🎓 Udemy Coupons | Courses
The first channel in Telegram that offers free Udemy coupons
https://news.1rj.ru/str/DataScienceC
😀 ML Research Hub
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
https://news.1rj.ru/str/DataScienceT
💬 Data Science Chat
An active community group for discussing data challenges and networking with peers.
https://news.1rj.ru/str/DataScience9
🐍 Python Arab| بايثون عربي
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://news.1rj.ru/str/PythonArab
🖊 Data Science Jupyter Notebooks
Explore the world of Data Science through Jupyter Notebooks—insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://news.1rj.ru/str/DataScienceN
📺 Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://news.1rj.ru/str/DataScienceV
📈 Data Analytics
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
https://news.1rj.ru/str/DataAnalyticsX
🎧 Learn Python Hub
Master Python with step-by-step courses – from basics to advanced projects and practical applications.
https://news.1rj.ru/str/Python53
⭐️ Research Papers
Professional Academic Writing & Simulation Services
https://news.1rj.ru/str/DataScienceY
━━━━━━━━━━━━━━━━━━
Admin: @HusseinSheikho
Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today!
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://news.1rj.ru/str/CodeProgrammer
Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.
https://news.1rj.ru/str/DataScienceM
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills.
https://news.1rj.ru/str/DataScience4
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://news.1rj.ru/str/DataScienceQ
Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://news.1rj.ru/str/datasets1
The first channel in Telegram that offers free Udemy coupons
https://news.1rj.ru/str/DataScienceC
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
https://news.1rj.ru/str/DataScienceT
An active community group for discussing data challenges and networking with peers.
https://news.1rj.ru/str/DataScience9
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://news.1rj.ru/str/PythonArab
Explore the world of Data Science through Jupyter Notebooks—insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://news.1rj.ru/str/DataScienceN
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://news.1rj.ru/str/DataScienceV
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
https://news.1rj.ru/str/DataAnalyticsX
Master Python with step-by-step courses – from basics to advanced projects and practical applications.
https://news.1rj.ru/str/Python53
Professional Academic Writing & Simulation Services
https://news.1rj.ru/str/DataScienceY
━━━━━━━━━━━━━━━━━━
Admin: @HusseinSheikho
Please open Telegram to view this post
VIEW IN TELEGRAM
❤1
✨CauSight: Learning to Supersense for Visual Causal Discovery
📝 Summary:
CauSight is a novel vision-language model for visual causal discovery, inferring cause-effect relations in images. It uses the VCG-32K dataset and Tree-of-Causal-Thought, significantly outperforming GPT-4.1 with a threefold performance boost.
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01827
• PDF: https://arxiv.org/pdf/2512.01827
• Github: https://github.com/OpenCausaLab/CauSight
🔹 Models citing this paper:
• https://huggingface.co/OpenCausaLab/CauSight
✨ Datasets citing this paper:
• https://huggingface.co/datasets/OpenCausaLab/VCG-32K
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#VisualCausalDiscovery #VisionLanguageModels #AI #DeepLearning #CausalInference
📝 Summary:
CauSight is a novel vision-language model for visual causal discovery, inferring cause-effect relations in images. It uses the VCG-32K dataset and Tree-of-Causal-Thought, significantly outperforming GPT-4.1 with a threefold performance boost.
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01827
• PDF: https://arxiv.org/pdf/2512.01827
• Github: https://github.com/OpenCausaLab/CauSight
🔹 Models citing this paper:
• https://huggingface.co/OpenCausaLab/CauSight
✨ Datasets citing this paper:
• https://huggingface.co/datasets/OpenCausaLab/VCG-32K
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#VisualCausalDiscovery #VisionLanguageModels #AI #DeepLearning #CausalInference
✨POLARIS: Projection-Orthogonal Least Squares for Robust and Adaptive Inversion in Diffusion Models
📝 Summary:
POLARIS minimizes approximate noise errors in diffusion models during image inversion. It robustly treats the guidance scale as a step-wise variable, significantly improving image editing and restoration accuracy by reducing errors at each step.
🔹 Publication Date: Published on Nov 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.00369
• PDF: https://arxiv.org/pdf/2512.00369
• Project Page: https://polaris-code-official.github.io/
• Github: https://github.com/Chatonz/POLARIS
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#DiffusionModels #ImageProcessing #AI #MachineLearning #ComputerVision
📝 Summary:
POLARIS minimizes approximate noise errors in diffusion models during image inversion. It robustly treats the guidance scale as a step-wise variable, significantly improving image editing and restoration accuracy by reducing errors at each step.
🔹 Publication Date: Published on Nov 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.00369
• PDF: https://arxiv.org/pdf/2512.00369
• Project Page: https://polaris-code-official.github.io/
• Github: https://github.com/Chatonz/POLARIS
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#DiffusionModels #ImageProcessing #AI #MachineLearning #ComputerVision
❤2
✨Flow Straighter and Faster: Efficient One-Step Generative Modeling via MeanFlow on Rectified Trajectories
📝 Summary:
Rectified MeanFlow enables efficient one-step generative modeling. It achieves this by modeling the mean velocity field on a single-step rectified trajectory with a truncation heuristic, improving both sample quality and training efficiency over prior methods.
🔹 Publication Date: Published on Nov 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.23342
• PDF: https://arxiv.org/pdf/2511.23342
• Github: https://github.com/Xinxi-Zhang/Re-MeanFlow
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#GenerativeAI #MachineLearning #DeepLearning #AIResearch #MeanFlow
📝 Summary:
Rectified MeanFlow enables efficient one-step generative modeling. It achieves this by modeling the mean velocity field on a single-step rectified trajectory with a truncation heuristic, improving both sample quality and training efficiency over prior methods.
🔹 Publication Date: Published on Nov 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.23342
• PDF: https://arxiv.org/pdf/2511.23342
• Github: https://github.com/Xinxi-Zhang/Re-MeanFlow
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#GenerativeAI #MachineLearning #DeepLearning #AIResearch #MeanFlow
👍1
✨MEGConformer: Conformer-Based MEG Decoder for Robust Speech and Phoneme Classification
📝 Summary:
Conformer-based decoders were adapted for MEG signals to perform Speech Detection and Phoneme Classification. Using MEG-oriented augmentations and normalization, their systems achieved high performance, surpassing competition baselines and ranking within the top-10 in both tasks.
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01443
• PDF: https://arxiv.org/pdf/2512.01443
• Github: https://github.com/neural2speech/libribrain-experiments
🔹 Models citing this paper:
• https://huggingface.co/zuazo/megconformer-speech-detection
• https://huggingface.co/zuazo/megconformer-phoneme-classification
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#MEGConformer #MEG #SpeechProcessing #Neuroscience #AI
📝 Summary:
Conformer-based decoders were adapted for MEG signals to perform Speech Detection and Phoneme Classification. Using MEG-oriented augmentations and normalization, their systems achieved high performance, surpassing competition baselines and ranking within the top-10 in both tasks.
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01443
• PDF: https://arxiv.org/pdf/2512.01443
• Github: https://github.com/neural2speech/libribrain-experiments
🔹 Models citing this paper:
• https://huggingface.co/zuazo/megconformer-speech-detection
• https://huggingface.co/zuazo/megconformer-phoneme-classification
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#MEGConformer #MEG #SpeechProcessing #Neuroscience #AI
Media is too big
VIEW IN TELEGRAM
✨Generative Video Motion Editing with 3D Point Tracks
📝 Summary:
This paper presents a track-conditioned video-to-video framework for precise joint camera and object motion editing. It uses 3D point tracks to maintain spatiotemporal coherence and handle occlusions through explicit depth cues. This enables diverse motion edits.
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.02015
• PDF: https://arxiv.org/pdf/2512.02015
• Project Page: https://edit-by-track.github.io/
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#VideoEditing #GenerativeAI #ComputerVision #3DTracking #DeepLearning
📝 Summary:
This paper presents a track-conditioned video-to-video framework for precise joint camera and object motion editing. It uses 3D point tracks to maintain spatiotemporal coherence and handle occlusions through explicit depth cues. This enables diverse motion edits.
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.02015
• PDF: https://arxiv.org/pdf/2512.02015
• Project Page: https://edit-by-track.github.io/
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#VideoEditing #GenerativeAI #ComputerVision #3DTracking #DeepLearning
❤1👍1