π 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!
π° Machine Learning with Python
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://t.me/CodeProgrammer
π 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://t.me/DataScienceM
π§ Code With Python
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA β perfect for learning, coding, and mastering key programming skills.
https://t.me/DataScience4
π― PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.me/DataScienceQ
πΎ Kaggle Data Hub
Your go-to hub for Kaggle datasets β explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.me/datasets1
π§βπ Udemy Coupons | Courses
The first channel in Telegram that offers free Udemy coupons
https://t.me/DataScienceC
π ML Research Hub
Advancing research in Machine Learning β practical insights, tools, and techniques for researchers.
https://t.me/DataScienceT
π¬ Data Science Chat
An active community group for discussing data challenges and networking with peers.
https://t.me/DataScience9
π Python Arab| Ψ¨Ψ§ΩΨ«ΩΩ ΨΉΨ±Ψ¨Ω
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.me/PythonArab
π Data Science Jupyter Notebooks
Explore the world of Data Science through Jupyter Notebooksβinsights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://t.me/DataScienceN
πΊ Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.me/DataScienceV
π Data Analytics
Dive into the world of Data Analytics β uncover insights, explore trends, and master data-driven decision making.
https://t.me/DataAnalyticsX
π§ Learn Python Hub
Master Python with step-by-step courses β from basics to advanced projects and practical applications.
https://t.me/Python53
βοΈ Research Papers
Professional Academic Writing & Simulation Services
https://t.me/DataScienceY
ββββββββββββββββββ
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://t.me/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://t.me/DataScienceM
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA β perfect for learning, coding, and mastering key programming skills.
https://t.me/DataScience4
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.me/DataScienceQ
Your go-to hub for Kaggle datasets β explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.me/datasets1
The first channel in Telegram that offers free Udemy coupons
https://t.me/DataScienceC
Advancing research in Machine Learning β practical insights, tools, and techniques for researchers.
https://t.me/DataScienceT
An active community group for discussing data challenges and networking with peers.
https://t.me/DataScience9
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.me/PythonArab
Explore the world of Data Science through Jupyter Notebooksβinsights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://t.me/DataScienceN
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.me/DataScienceV
Dive into the world of Data Analytics β uncover insights, explore trends, and master data-driven decision making.
https://t.me/DataAnalyticsX
Master Python with step-by-step courses β from basics to advanced projects and practical applications.
https://t.me/Python53
Professional Academic Writing & Simulation Services
https://t.me/DataScienceY
ββββββββββββββββββ
Admin: @HusseinSheikho
Please open Telegram to view this post
VIEW IN TELEGRAM
β€2
β¨Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting
π Summary:
Dolphin is a novel multimodal model for document image parsing. It uses an analyze-then-parse approach with heterogeneous anchor prompting, achieving state-of-the-art performance and superior efficiency.
πΉ Publication Date: Published on May 20, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2505.14059
β’ PDF: https://arxiv.org/pdf/2505.14059
β’ Github: https://github.com/bytedance/dolphin
==================================
For more data science resources:
β https://t.me/DataScienceT
#DocumentParsing #MultimodalAI #DeepLearning #ComputerVision #AI
π Summary:
Dolphin is a novel multimodal model for document image parsing. It uses an analyze-then-parse approach with heterogeneous anchor prompting, achieving state-of-the-art performance and superior efficiency.
πΉ Publication Date: Published on May 20, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2505.14059
β’ PDF: https://arxiv.org/pdf/2505.14059
β’ Github: https://github.com/bytedance/dolphin
==================================
For more data science resources:
β https://t.me/DataScienceT
#DocumentParsing #MultimodalAI #DeepLearning #ComputerVision #AI
β€1
π 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!
π° Machine Learning with Python
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://t.me/CodeProgrammer
π 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://t.me/DataScienceM
π§ Code With Python
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA β perfect for learning, coding, and mastering key programming skills.
https://t.me/DataScience4
π― PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.me/DataScienceQ
πΎ Kaggle Data Hub
Your go-to hub for Kaggle datasets β explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.me/datasets1
π§βπ Udemy Coupons | Courses
The first channel in Telegram that offers free Udemy coupons
https://t.me/DataScienceC
π ML Research Hub
Advancing research in Machine Learning β practical insights, tools, and techniques for researchers.
https://t.me/DataScienceT
π¬ Data Science Chat
An active community group for discussing data challenges and networking with peers.
https://t.me/DataScience9
π Python Arab| Ψ¨Ψ§ΩΨ«ΩΩ ΨΉΨ±Ψ¨Ω
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.me/PythonArab
π Data Science Jupyter Notebooks
Explore the world of Data Science through Jupyter Notebooksβinsights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://t.me/DataScienceN
πΊ Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.me/DataScienceV
π Data Analytics
Dive into the world of Data Analytics β uncover insights, explore trends, and master data-driven decision making.
https://t.me/DataAnalyticsX
π§ Learn Python Hub
Master Python with step-by-step courses β from basics to advanced projects and practical applications.
https://t.me/Python53
βοΈ Research Papers
Professional Academic Writing & Simulation Services
https://t.me/DataScienceY
ββββββββββββββββββ
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://t.me/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://t.me/DataScienceM
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA β perfect for learning, coding, and mastering key programming skills.
https://t.me/DataScience4
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.me/DataScienceQ
Your go-to hub for Kaggle datasets β explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.me/datasets1
The first channel in Telegram that offers free Udemy coupons
https://t.me/DataScienceC
Advancing research in Machine Learning β practical insights, tools, and techniques for researchers.
https://t.me/DataScienceT
An active community group for discussing data challenges and networking with peers.
https://t.me/DataScience9
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.me/PythonArab
Explore the world of Data Science through Jupyter Notebooksβinsights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://t.me/DataScienceN
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.me/DataScienceV
Dive into the world of Data Analytics β uncover insights, explore trends, and master data-driven decision making.
https://t.me/DataAnalyticsX
Master Python with step-by-step courses β from basics to advanced projects and practical applications.
https://t.me/Python53
Professional Academic Writing & Simulation Services
https://t.me/DataScienceY
ββββββββββββββββββ
Admin: @HusseinSheikho
Please open Telegram to view this post
VIEW IN TELEGRAM
β€1
β¨Youtu-Agent: Scaling Agent Productivity with Automated Generation and Hybrid Policy Optimization
π Summary:
Youtu-Agent scales LLM agent productivity, automating generation and enabling continuous evolution. Its hybrid optimization, using in-context learning and scalable reinforcement learning, yields top performance and boosted capabilities.
πΉ Publication Date: Published on Dec 31, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.24615
β’ PDF: https://arxiv.org/pdf/2512.24615
β’ Project Page: https://tencentcloudadp.github.io/youtu-agent/
β’ Github: https://github.com/TencentCloudADP/youtu-tip
==================================
For more data science resources:
β https://t.me/DataScienceT
#LLM #AIAgents #ReinforcementLearning #MachineLearning #AI
π Summary:
Youtu-Agent scales LLM agent productivity, automating generation and enabling continuous evolution. Its hybrid optimization, using in-context learning and scalable reinforcement learning, yields top performance and boosted capabilities.
πΉ Publication Date: Published on Dec 31, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.24615
β’ PDF: https://arxiv.org/pdf/2512.24615
β’ Project Page: https://tencentcloudadp.github.io/youtu-agent/
β’ Github: https://github.com/TencentCloudADP/youtu-tip
==================================
For more data science resources:
β https://t.me/DataScienceT
#LLM #AIAgents #ReinforcementLearning #MachineLearning #AI
β¨SenseNova-MARS: Empowering Multimodal Agentic Reasoning and Search via Reinforcement Learning
π Summary:
SenseNova-MARS empowers Vision-Language Models with interleaved visual reasoning and dynamic tool use like search and cropping via reinforcement learning. It achieves state-of-the-art performance on complex visual tasks, outperforming proprietary models on new and existing benchmarks.
πΉ Publication Date: Published on Dec 30, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.24330
β’ PDF: https://arxiv.org/pdf/2512.24330
β’ Github: https://github.com/OpenSenseNova/SenseNova-MARS
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/sensenova/SenseNova-MARS-Data
β’ https://huggingface.co/datasets/sensenova/HR-MMSearch
==================================
For more data science resources:
β https://t.me/DataScienceT
#MultimodalAI #ReinforcementLearning #VisionLanguageModels #AgenticAI #ComputerVision
π Summary:
SenseNova-MARS empowers Vision-Language Models with interleaved visual reasoning and dynamic tool use like search and cropping via reinforcement learning. It achieves state-of-the-art performance on complex visual tasks, outperforming proprietary models on new and existing benchmarks.
πΉ Publication Date: Published on Dec 30, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.24330
β’ PDF: https://arxiv.org/pdf/2512.24330
β’ Github: https://github.com/OpenSenseNova/SenseNova-MARS
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/sensenova/SenseNova-MARS-Data
β’ https://huggingface.co/datasets/sensenova/HR-MMSearch
==================================
For more data science resources:
β https://t.me/DataScienceT
#MultimodalAI #ReinforcementLearning #VisionLanguageModels #AgenticAI #ComputerVision
β€1
This media is not supported in your browser
VIEW IN TELEGRAM
β¨Avatar Forcing: Real-Time Interactive Head Avatar Generation for Natural Conversation
π Summary:
Avatar Forcing creates real-time interactive talking head avatars. It uses diffusion forcing for low-latency reactions to user input and a label-free preference optimization for expressive, preferred motion, achieving 6.8x speedup.
πΉ Publication Date: Published on Jan 2
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2601.00664
β’ PDF: https://arxiv.org/pdf/2601.00664
β’ Project Page: https://taekyungki.github.io/AvatarForcing/
β’ Github: https://github.com/TaekyungKi/AvatarForcing
==================================
For more data science resources:
β https://t.me/DataScienceT
#AvatarGeneration #RealTimeAI #GenerativeAI #ComputerVision #AIResearch
π Summary:
Avatar Forcing creates real-time interactive talking head avatars. It uses diffusion forcing for low-latency reactions to user input and a label-free preference optimization for expressive, preferred motion, achieving 6.8x speedup.
πΉ Publication Date: Published on Jan 2
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2601.00664
β’ PDF: https://arxiv.org/pdf/2601.00664
β’ Project Page: https://taekyungki.github.io/AvatarForcing/
β’ Github: https://github.com/TaekyungKi/AvatarForcing
==================================
For more data science resources:
β https://t.me/DataScienceT
#AvatarGeneration #RealTimeAI #GenerativeAI #ComputerVision #AIResearch
β¨Deep Delta Learning
π Summary:
The efficacy of deep residual networks is fundamentally predicated on the identity shortcut connection. While this mechanism effectively mitigates the vanishing gradient problem, it imposes a strictly...
πΉ Publication Date: Published on Jan 1
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2601.00417
β’ PDF: https://arxiv.org/pdf/2601.00417
β’ Github: https://github.com/yifanzhang-pro/deep-delta-learning
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
The efficacy of deep residual networks is fundamentally predicated on the identity shortcut connection. While this mechanism effectively mitigates the vanishing gradient problem, it imposes a strictly...
πΉ Publication Date: Published on Jan 1
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2601.00417
β’ PDF: https://arxiv.org/pdf/2601.00417
β’ Github: https://github.com/yifanzhang-pro/deep-delta-learning
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β¨Fast-weight Product Key Memory
π Summary:
FwPKM introduces a dynamic, fast-weight episodic memory mechanism for sequence modeling that balances storage capacity and efficiency, achieving strong performance on long-context tasks like Needle in...
πΉ Publication Date: Published on Jan 2
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2601.00671
β’ PDF: https://arxiv.org/pdf/2601.00671
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
FwPKM introduces a dynamic, fast-weight episodic memory mechanism for sequence modeling that balances storage capacity and efficiency, achieving strong performance on long-context tasks like Needle in...
πΉ Publication Date: Published on Jan 2
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2601.00671
β’ PDF: https://arxiv.org/pdf/2601.00671
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β¨Taming Hallucinations: Boosting MLLMs' Video Understanding via Counterfactual Video Generation
π Summary:
MLLMs struggle with hallucinations on counterfactual videos. DualityForge synthesizes counterfactual video data and QA pairs through diffusion-based editing to address this. This method significantly reduces model hallucinations and improves general performance.
πΉ Publication Date: Published on Dec 30, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.24271
β’ PDF: https://arxiv.org/pdf/2512.24271
β’ Project Page: https://amap-ml.github.io/Taming-Hallucinations/
β’ Github: https://github.com/AMAP-ML/Taming-Hallucinations
==================================
For more data science resources:
β https://t.me/DataScienceT
#MLLMs #VideoUnderstanding #AIHallucinations #GenerativeAI #MachineLearning
π Summary:
MLLMs struggle with hallucinations on counterfactual videos. DualityForge synthesizes counterfactual video data and QA pairs through diffusion-based editing to address this. This method significantly reduces model hallucinations and improves general performance.
πΉ Publication Date: Published on Dec 30, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.24271
β’ PDF: https://arxiv.org/pdf/2512.24271
β’ Project Page: https://amap-ml.github.io/Taming-Hallucinations/
β’ Github: https://github.com/AMAP-ML/Taming-Hallucinations
==================================
For more data science resources:
β https://t.me/DataScienceT
#MLLMs #VideoUnderstanding #AIHallucinations #GenerativeAI #MachineLearning
This media is not supported in your browser
VIEW IN TELEGRAM
β¨NeoVerse: Enhancing 4D World Model with in-the-wild Monocular Videos
π Summary:
NeoVerse is a 4D world model for reconstruction and video generation. It scales to in-the-wild monocular videos using pose-free feed-forward reconstruction and online degradation simulation, achieving state-of-the-art performance.
πΉ Publication Date: Published on Jan 1
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2601.00393
β’ PDF: https://arxiv.org/pdf/2601.00393
β’ Project Page: https://neoverse-4d.github.io/
β’ Github: https://neoverse-4d.github.io
==================================
For more data science resources:
β https://t.me/DataScienceT
#4DWorldModel #VideoGeneration #ComputerVision #DeepLearning #AI
π Summary:
NeoVerse is a 4D world model for reconstruction and video generation. It scales to in-the-wild monocular videos using pose-free feed-forward reconstruction and online degradation simulation, achieving state-of-the-art performance.
πΉ Publication Date: Published on Jan 1
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2601.00393
β’ PDF: https://arxiv.org/pdf/2601.00393
β’ Project Page: https://neoverse-4d.github.io/
β’ Github: https://neoverse-4d.github.io
==================================
For more data science resources:
β https://t.me/DataScienceT
#4DWorldModel #VideoGeneration #ComputerVision #DeepLearning #AI
β¨MorphAny3D: Unleashing the Power of Structured Latent in 3D Morphing
π Summary:
MorphAny3D offers a training-free framework for high-quality 3D morphing, even across categories. It leverages Structured Latent representations with novel attention mechanisms MCA, TFSA for structural coherence and temporal consistency. This achieves state-of-the-art results and supports advance...
πΉ Publication Date: Published on Jan 1
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2601.00204
β’ PDF: https://arxiv.org/pdf/2601.00204
β’ Project Page: https://xiaokunsun.github.io/MorphAny3D.github.io
β’ Github: https://github.com/XiaokunSun/MorphAny3D
==================================
For more data science resources:
β https://t.me/DataScienceT
#3DMorphing #ComputerGraphics #DeepLearning #StructuredLatent #AIResearch
π Summary:
MorphAny3D offers a training-free framework for high-quality 3D morphing, even across categories. It leverages Structured Latent representations with novel attention mechanisms MCA, TFSA for structural coherence and temporal consistency. This achieves state-of-the-art results and supports advance...
πΉ Publication Date: Published on Jan 1
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2601.00204
β’ PDF: https://arxiv.org/pdf/2601.00204
β’ Project Page: https://xiaokunsun.github.io/MorphAny3D.github.io
β’ Github: https://github.com/XiaokunSun/MorphAny3D
==================================
For more data science resources:
β https://t.me/DataScienceT
#3DMorphing #ComputerGraphics #DeepLearning #StructuredLatent #AIResearch
β¨Nested Learning: The Illusion of Deep Learning Architectures
π Summary:
Nested Learning NL models ML as nested optimization problems. It enables expressive algorithms for higher-order learning and continual adaptation, introducing optimizers, self-modifying models, and continuum memory systems.
πΉ Publication Date: Published on Dec 31, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.24695
β’ PDF: https://arxiv.org/pdf/2512.24695
==================================
For more data science resources:
β https://t.me/DataScienceT
#NestedLearning #MachineLearning #DeepLearning #Optimization #AI
π Summary:
Nested Learning NL models ML as nested optimization problems. It enables expressive algorithms for higher-order learning and continual adaptation, introducing optimizers, self-modifying models, and continuum memory systems.
πΉ Publication Date: Published on Dec 31, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.24695
β’ PDF: https://arxiv.org/pdf/2512.24695
==================================
For more data science resources:
β https://t.me/DataScienceT
#NestedLearning #MachineLearning #DeepLearning #Optimization #AI
nature papers: 1400$
Q1 and Q2 papers 900$
Q3 and Q4 papers 500$
Doctoral thesis (complete) 700$
M.S thesis 300$
paper simulation 200$
Contact me
https://t.me/m/-nTmpj5vYzNk
Q1 and Q2 papers 900$
Q3 and Q4 papers 500$
Doctoral thesis (complete) 700$
M.S thesis 300$
paper simulation 200$
Contact me
https://t.me/m/-nTmpj5vYzNk
ML Research Hub pinned Β«nature papers: 1400$ Q1 and Q2 papers 900$ Q3 and Q4 papers 500$ Doctoral thesis (complete) 700$ M.S thesis 300$ paper simulation 200$ Contact me https://t.me/m/-nTmpj5vYzNkΒ»
This media is not supported in your browser
VIEW IN TELEGRAM
β¨AdaGaR: Adaptive Gabor Representation for Dynamic Scene Reconstruction
π Summary:
AdaGaR reconstructs dynamic 3D scenes from monocular video. It introduces an Adaptive Gabor Representation for detail and stability, and Cubic Hermite Splines for temporal continuity. This method achieves state-of-the-art performance.
πΉ Publication Date: Published on Jan 2
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2601.00796
β’ PDF: https://arxiv.org/pdf/2601.00796
β’ Project Page: https://jiewenchan.github.io/AdaGaR/
==================================
For more data science resources:
β https://t.me/DataScienceT
#3DReconstruction #ComputerVision #DynamicScenes #MonocularVideo #GaborRepresentation
π Summary:
AdaGaR reconstructs dynamic 3D scenes from monocular video. It introduces an Adaptive Gabor Representation for detail and stability, and Cubic Hermite Splines for temporal continuity. This method achieves state-of-the-art performance.
πΉ Publication Date: Published on Jan 2
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2601.00796
β’ PDF: https://arxiv.org/pdf/2601.00796
β’ Project Page: https://jiewenchan.github.io/AdaGaR/
==================================
For more data science resources:
β https://t.me/DataScienceT
#3DReconstruction #ComputerVision #DynamicScenes #MonocularVideo #GaborRepresentation
β€1
β¨InfoSynth: Information-Guided Benchmark Synthesis for LLMs
π Summary:
InfoSynth automatically generates novel and diverse coding benchmarks for LLMs. It uses information-theoretic metrics and genetic algorithms to create scalable self-verifying problems, overcoming manual effort and training data contamination.
πΉ Publication Date: Published on Jan 2
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2601.00575
β’ PDF: https://arxiv.org/pdf/2601.00575
β’ Project Page: https://ishirgarg.github.io/infosynth_web/
β’ Github: https://github.com/ishirgarg/infosynth
==================================
For more data science resources:
β https://t.me/DataScienceT
#LLM #AI #Benchmarking #GenerativeAI #DeepLearning
π Summary:
InfoSynth automatically generates novel and diverse coding benchmarks for LLMs. It uses information-theoretic metrics and genetic algorithms to create scalable self-verifying problems, overcoming manual effort and training data contamination.
πΉ Publication Date: Published on Jan 2
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2601.00575
β’ PDF: https://arxiv.org/pdf/2601.00575
β’ Project Page: https://ishirgarg.github.io/infosynth_web/
β’ Github: https://github.com/ishirgarg/infosynth
==================================
For more data science resources:
β https://t.me/DataScienceT
#LLM #AI #Benchmarking #GenerativeAI #DeepLearning
β¨Diversity or Precision? A Deep Dive into Next Token Prediction
π Summary:
This paper proposes a pre-training objective that reshapes the token-output distribution for better RL exploration. It uses reward-shaping to balance diversity and precision in next-token prediction. Contrary to intuition, a precision-oriented prior surprisingly yields a superior exploration spac...
πΉ Publication Date: Published on Dec 28, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.22955
β’ PDF: https://arxiv.org/pdf/2512.22955
==================================
For more data science resources:
β https://t.me/DataScienceT
#NextTokenPrediction #ReinforcementLearning #LLM #NLP #AIResearch
π Summary:
This paper proposes a pre-training objective that reshapes the token-output distribution for better RL exploration. It uses reward-shaping to balance diversity and precision in next-token prediction. Contrary to intuition, a precision-oriented prior surprisingly yields a superior exploration spac...
πΉ Publication Date: Published on Dec 28, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.22955
β’ PDF: https://arxiv.org/pdf/2512.22955
==================================
For more data science resources:
β https://t.me/DataScienceT
#NextTokenPrediction #ReinforcementLearning #LLM #NLP #AIResearch
β€1
β¨OmniVCus: Feedforward Subject-driven Video Customization with Multimodal Control Conditions
π Summary:
OmniVCus introduces a system for feedforward multi-subject video customization with multimodal controls. It proposes a data pipeline, VideoCus-Factory, and a diffusion Transformer framework with novel embedding mechanisms. This enables more subjects and precise editing, significantly outperformin...
πΉ Publication Date: Published on Jun 29, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2506.23361
β’ PDF: https://arxiv.org/pdf/2506.23361
β’ Project Page: https://caiyuanhao1998.github.io/project/OmniVCus/
β’ Github: https://github.com/caiyuanhao1998/Open-OmniVCus
πΉ Models citing this paper:
β’ https://huggingface.co/CaiYuanhao/OmniVCus
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/CaiYuanhao/OmniVCus
β’ https://huggingface.co/datasets/CaiYuanhao/OmniVCus-Test
β’ https://huggingface.co/datasets/CaiYuanhao/OmniVCus-Train
==================================
For more data science resources:
β https://t.me/DataScienceT
#VideoGeneration #DiffusionModels #MultimodalAI #DeepLearning #ComputerVision
π Summary:
OmniVCus introduces a system for feedforward multi-subject video customization with multimodal controls. It proposes a data pipeline, VideoCus-Factory, and a diffusion Transformer framework with novel embedding mechanisms. This enables more subjects and precise editing, significantly outperformin...
πΉ Publication Date: Published on Jun 29, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2506.23361
β’ PDF: https://arxiv.org/pdf/2506.23361
β’ Project Page: https://caiyuanhao1998.github.io/project/OmniVCus/
β’ Github: https://github.com/caiyuanhao1998/Open-OmniVCus
πΉ Models citing this paper:
β’ https://huggingface.co/CaiYuanhao/OmniVCus
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/CaiYuanhao/OmniVCus
β’ https://huggingface.co/datasets/CaiYuanhao/OmniVCus-Test
β’ https://huggingface.co/datasets/CaiYuanhao/OmniVCus-Train
==================================
For more data science resources:
β https://t.me/DataScienceT
#VideoGeneration #DiffusionModels #MultimodalAI #DeepLearning #ComputerVision
arXiv.org
OmniVCus: Feedforward Subject-driven Video Customization with...
Existing feedforward subject-driven video customization methods mainly study single-subject scenarios due to the difficulty of constructing multi-subject training data pairs. Another challenging...
β€1
Media is too big
VIEW IN TELEGRAM
OnSpace Mobile App builder: Build AI Apps in minutes
Visit website: https://www.onspace.ai/?via=tg_datas
Or Download app:https://onspace.onelink.me/za8S/h1jb6sb9?c=datas
With OnSpace, you can build website or AI Mobile Apps by chatting with AI, and publish to PlayStore or AppStore.
What will you get:
βοΈ Create app or website by chatting with AI;
βοΈ Integrate with Any top AI power just by giving order (like Sora2, Nanobanan Pro & Gemini 3 Pro);
βοΈ Download APK,AAB file, publish to AppStore.
βοΈ Add payments and monetize like in-app-purchase and Stripe.
βοΈ Functional login & signup.
βοΈ Database + dashboard in minutes.
βοΈ Full tutorial on YouTube and within 1 day customer service
Visit website: https://www.onspace.ai/?via=tg_datas
Or Download app:https://onspace.onelink.me/za8S/h1jb6sb9?c=datas
With OnSpace, you can build website or AI Mobile Apps by chatting with AI, and publish to PlayStore or AppStore.
What will you get:
Please open Telegram to view this post
VIEW IN TELEGRAM
β€2