π Master Data Science & Programming!
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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
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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.
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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
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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
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β¨TraPO: A Semi-Supervised Reinforcement Learning Framework for Boosting LLM Reasoning
π Summary:
A semi-supervised reinforcement learning with verifiable rewards approach uses a small labeled dataset to guide training on unlabeled samples, achieving high efficiency and accuracy in mathematical re...
πΉ Publication Date: Published on Dec 15
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.13106
β’ PDF: https://arxiv.org/pdf/2512.13106
β’ Github: https://github.com/ShenzhiYang2000/TRAPO
==================================
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β https://t.me/DataScienceT
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π Summary:
A semi-supervised reinforcement learning with verifiable rewards approach uses a small labeled dataset to guide training on unlabeled samples, achieving high efficiency and accuracy in mathematical re...
πΉ Publication Date: Published on Dec 15
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.13106
β’ PDF: https://arxiv.org/pdf/2512.13106
β’ Github: https://github.com/ShenzhiYang2000/TRAPO
==================================
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β https://t.me/DataScienceT
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β€1
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β¨CRISP: Contact-Guided Real2Sim from Monocular Video with Planar Scene Primitives
π Summary:
CRISP recovers simulation-ready human motion and scene geometry from monocular video. It uses planar primitive fitting and human-scene contact modeling for robust geometry, driving a humanoid via RL for physical plausibility. This reduces motion tracking failures and speeds up RL simulation, adva...
πΉ Publication Date: Published on Dec 16
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.14696
β’ PDF: https://arxiv.org/pdf/2512.14696
β’ Project Page: https://crisp-real2sim.github.io/CRISP-Real2Sim/
β’ Github: https://crisp-real2sim.github.io/CRISP-Real2Sim/%7D%7B%5Ctextcolor%7Bcyan%7D%7B%7Bcrisp-real2sim.github.io/CRISP-Real2Sim%7D%7D%7D
==================================
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β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
CRISP recovers simulation-ready human motion and scene geometry from monocular video. It uses planar primitive fitting and human-scene contact modeling for robust geometry, driving a humanoid via RL for physical plausibility. This reduces motion tracking failures and speeds up RL simulation, adva...
πΉ Publication Date: Published on Dec 16
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.14696
β’ PDF: https://arxiv.org/pdf/2512.14696
β’ Project Page: https://crisp-real2sim.github.io/CRISP-Real2Sim/
β’ Github: https://crisp-real2sim.github.io/CRISP-Real2Sim/%7D%7B%5Ctextcolor%7Bcyan%7D%7B%7Bcrisp-real2sim.github.io/CRISP-Real2Sim%7D%7D%7D
==================================
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β€1
β¨UAGLNet: Uncertainty-Aggregated Global-Local Fusion Network with Cooperative CNN-Transformer for Building Extraction
π Summary:
UAGLNet addresses building extraction challenges by integrating global and local features through a hybrid CNN and transformer cooperative encoder, intermediate interaction block, and uncertainty-aggr...
πΉ Publication Date: Published on Dec 15
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.12941
β’ PDF: https://arxiv.org/pdf/2512.12941
πΉ Models citing this paper:
β’ https://huggingface.co/ldxxx/UAGLNet_Backbone
β’ https://huggingface.co/ldxxx/UAGLNet_Inria
β’ https://huggingface.co/ldxxx/UAGLNet_WHU
==================================
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β https://t.me/DataScienceT
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π Summary:
UAGLNet addresses building extraction challenges by integrating global and local features through a hybrid CNN and transformer cooperative encoder, intermediate interaction block, and uncertainty-aggr...
πΉ Publication Date: Published on Dec 15
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.12941
β’ PDF: https://arxiv.org/pdf/2512.12941
πΉ Models citing this paper:
β’ https://huggingface.co/ldxxx/UAGLNet_Backbone
β’ https://huggingface.co/ldxxx/UAGLNet_Inria
β’ https://huggingface.co/ldxxx/UAGLNet_WHU
==================================
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β https://t.me/DataScienceT
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β¨CoSPlan: Corrective Sequential Planning via Scene Graph Incremental Updates
π Summary:
VLMs struggle with error-prone vision-based sequential planning tasks, but Scene Graph Incremental updates (SGI) improves their performance by introducing intermediate reasoning steps. AI-generated su...
πΉ Publication Date: Published on Dec 11
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.10342
β’ PDF: https://arxiv.org/pdf/2512.10342
==================================
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β https://t.me/DataScienceT
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π Summary:
VLMs struggle with error-prone vision-based sequential planning tasks, but Scene Graph Incremental updates (SGI) improves their performance by introducing intermediate reasoning steps. AI-generated su...
πΉ Publication Date: Published on Dec 11
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.10342
β’ PDF: https://arxiv.org/pdf/2512.10342
==================================
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β https://t.me/DataScienceT
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β€2
β¨Hierarchical Dataset Selection for High-Quality Data Sharing
π Summary:
DaSH selects entire datasets from diverse sources to boost ML performance. It models utility hierarchically, outperforming existing methods by up to 26.2 percent accuracy with fewer resources. DaSH is robust for multi-source learning workflows.
πΉ Publication Date: Published on Dec 11
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.10952
β’ PDF: https://arxiv.org/pdf/2512.10952
==================================
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β https://t.me/DataScienceT
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π Summary:
DaSH selects entire datasets from diverse sources to boost ML performance. It models utility hierarchically, outperforming existing methods by up to 26.2 percent accuracy with fewer resources. DaSH is robust for multi-source learning workflows.
πΉ Publication Date: Published on Dec 11
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.10952
β’ PDF: https://arxiv.org/pdf/2512.10952
==================================
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β https://t.me/DataScienceT
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β¨Unveiling User Perceptions in the Generative AI Era: A Sentiment-Driven Evaluation of AI Educational Apps' Role in Digital Transformation of e-Teaching
π Summary:
User reviews of AI educational apps show predominantly positive sentiments, with homework helpers leading in accuracy and personalization. However, language and LMS apps lag due to instability and limited features. This highlights generative AIs potential for e-teaching despite challenges.
πΉ Publication Date: Published on Dec 12
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.11934
β’ PDF: https://arxiv.org/pdf/2512.11934
β’ Github: https://github.com/erfan-nourbakhsh/GenAI-EdSent
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/Erfan-Nourbakhsh/GenAI-EdSent
==================================
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β https://t.me/DataScienceT
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π Summary:
User reviews of AI educational apps show predominantly positive sentiments, with homework helpers leading in accuracy and personalization. However, language and LMS apps lag due to instability and limited features. This highlights generative AIs potential for e-teaching despite challenges.
πΉ Publication Date: Published on Dec 12
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.11934
β’ PDF: https://arxiv.org/pdf/2512.11934
β’ Github: https://github.com/erfan-nourbakhsh/GenAI-EdSent
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/Erfan-Nourbakhsh/GenAI-EdSent
==================================
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β https://t.me/DataScienceT
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β€1
β¨Universal Reasoning Model
π Summary:
The Universal Reasoning Model URM enhances Universal Transformers with short convolution and truncated backpropagation. This approach substantially improves reasoning performance on ARC-AGI tasks, achieving state-of-the-art results.
πΉ Publication Date: Published on Dec 16
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.14693
β’ PDF: https://arxiv.org/pdf/2512.14693
β’ Github: https://github.com/zitian-gao/URM
==================================
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β https://t.me/DataScienceT
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π Summary:
The Universal Reasoning Model URM enhances Universal Transformers with short convolution and truncated backpropagation. This approach substantially improves reasoning performance on ARC-AGI tasks, achieving state-of-the-art results.
πΉ Publication Date: Published on Dec 16
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.14693
β’ PDF: https://arxiv.org/pdf/2512.14693
β’ Github: https://github.com/zitian-gao/URM
==================================
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β https://t.me/DataScienceT
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β¨VABench: A Comprehensive Benchmark for Audio-Video Generation
π Summary:
VABench is a benchmark framework for evaluating audio-video generation models, covering text-to-audio-video, image-to-audio-video, and stereo audio-video tasks with 15 evaluation dimensions. AI-genera...
πΉ Publication Date: Published on Dec 10
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.09299
β’ PDF: https://arxiv.org/pdf/2512.09299
β’ Github: https://github.com/tanABCC/VABench
==================================
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β https://t.me/DataScienceT
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π Summary:
VABench is a benchmark framework for evaluating audio-video generation models, covering text-to-audio-video, image-to-audio-video, and stereo audio-video tasks with 15 evaluation dimensions. AI-genera...
πΉ Publication Date: Published on Dec 10
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.09299
β’ PDF: https://arxiv.org/pdf/2512.09299
β’ Github: https://github.com/tanABCC/VABench
==================================
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β https://t.me/DataScienceT
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β¨Can LLMs Guide Their Own Exploration? Gradient-Guided Reinforcement Learning for LLM Reasoning
π Summary:
G2RL, a gradient-guided reinforcement learning framework, enhances exploration in large language models by leveraging the model's own update geometry, leading to improved performance on various reason...
πΉ Publication Date: Published on Dec 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.15687
β’ PDF: https://arxiv.org/pdf/2512.15687
==================================
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β https://t.me/DataScienceT
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π Summary:
G2RL, a gradient-guided reinforcement learning framework, enhances exploration in large language models by leveraging the model's own update geometry, leading to improved performance on various reason...
πΉ Publication Date: Published on Dec 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.15687
β’ PDF: https://arxiv.org/pdf/2512.15687
==================================
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β https://t.me/DataScienceT
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β¨VTCBench: Can Vision-Language Models Understand Long Context with Vision-Text Compression?
π Summary:
A benchmark evaluates the performance of vision-language models on understanding long-context information compressed into dense visual representations, revealing significant limitations in capturing l...
πΉ Publication Date: Published on Dec 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.15649
β’ PDF: https://arxiv.org/pdf/2512.15649
β’ Github: https://github.com/Moenupa/VTCBench
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/MLLM-CL/VTCBench
==================================
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β https://t.me/DataScienceT
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π Summary:
A benchmark evaluates the performance of vision-language models on understanding long-context information compressed into dense visual representations, revealing significant limitations in capturing l...
πΉ Publication Date: Published on Dec 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.15649
β’ PDF: https://arxiv.org/pdf/2512.15649
β’ Github: https://github.com/Moenupa/VTCBench
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/MLLM-CL/VTCBench
==================================
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β https://t.me/DataScienceT
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β¨SCOPE: Prompt Evolution for Enhancing Agent Effectiveness
π Summary:
SCOPE enhances LLM agents' context management through prompt evolution, improving task success rates in dynamic environments without human intervention. AI-generated summary Large Language Model (LLM)...
πΉ Publication Date: Published on Dec 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.15374
β’ PDF: https://arxiv.org/pdf/2512.15374
β’ Github: https://github.com/JarvisPei/SCOPE
==================================
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β https://t.me/DataScienceT
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π Summary:
SCOPE enhances LLM agents' context management through prompt evolution, improving task success rates in dynamic environments without human intervention. AI-generated summary Large Language Model (LLM)...
πΉ Publication Date: Published on Dec 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.15374
β’ PDF: https://arxiv.org/pdf/2512.15374
β’ Github: https://github.com/JarvisPei/SCOPE
==================================
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β https://t.me/DataScienceT
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β¨Simultaneous Tactile-Visual Perception for Learning Multimodal Robot Manipulation
π Summary:
TacThru-UMI, a system combining a TacThru sensor with a Transformer-based Diffusion Policy, achieves superior performance in robotic manipulation tasks by integrating simultaneous multimodal perceptio...
πΉ Publication Date: Published on Dec 10
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.09851
β’ PDF: https://arxiv.org/pdf/2512.09851
β’ Project Page: https://tacthru.yuyang.li/
β’ Github: https://github.com/YuyangLee/TacThru
==================================
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β https://t.me/DataScienceT
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π Summary:
TacThru-UMI, a system combining a TacThru sensor with a Transformer-based Diffusion Policy, achieves superior performance in robotic manipulation tasks by integrating simultaneous multimodal perceptio...
πΉ Publication Date: Published on Dec 10
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.09851
β’ PDF: https://arxiv.org/pdf/2512.09851
β’ Project Page: https://tacthru.yuyang.li/
β’ Github: https://github.com/YuyangLee/TacThru
==================================
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β https://t.me/DataScienceT
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β¨DEER: Draft with Diffusion, Verify with Autoregressive Models
π Summary:
DEER is a novel speculative decoding framework that uses diffusion large language models for drafting, overcoming limitations of autoregressive drafters. It achieves significantly longer draft acceptance lengths and much faster LLM decoding speeds, outperforming existing methods like EAGLE-3.
πΉ Publication Date: Published on Dec 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.15176
β’ PDF: https://arxiv.org/pdf/2512.15176
==================================
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β https://t.me/DataScienceT
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π Summary:
DEER is a novel speculative decoding framework that uses diffusion large language models for drafting, overcoming limitations of autoregressive drafters. It achieves significantly longer draft acceptance lengths and much faster LLM decoding speeds, outperforming existing methods like EAGLE-3.
πΉ Publication Date: Published on Dec 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.15176
β’ PDF: https://arxiv.org/pdf/2512.15176
==================================
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β https://t.me/DataScienceT
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β¨Skyra: AI-Generated Video Detection via Grounded Artifact Reasoning
π Summary:
Skyra, a specialized multimodal large language model, detects and explains visual artifacts in AI-generated videos using a novel dataset and two-stage training strategy, outperforming existing methods...
πΉ Publication Date: Published on Dec 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.15693
β’ PDF: https://arxiv.org/pdf/2512.15693
β’ Project Page: https://joeleelyf.github.io/Skyra/
β’ Github: https://github.com/JoeLeelyf/Skyra
==================================
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β https://t.me/DataScienceT
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π Summary:
Skyra, a specialized multimodal large language model, detects and explains visual artifacts in AI-generated videos using a novel dataset and two-stage training strategy, outperforming existing methods...
πΉ Publication Date: Published on Dec 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.15693
β’ PDF: https://arxiv.org/pdf/2512.15693
β’ Project Page: https://joeleelyf.github.io/Skyra/
β’ Github: https://github.com/JoeLeelyf/Skyra
==================================
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β https://t.me/DataScienceT
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β¨Fast and Accurate Causal Parallel Decoding using Jacobi Forcing
π Summary:
Jacobi Forcing is a progressive distillation method that enables efficient parallel decoding of transformer-based models while maintaining performance, significantly reducing inference latency. AI-gen...
πΉ Publication Date: Published on Dec 16
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.14681
β’ PDF: https://arxiv.org/pdf/2512.14681
β’ Github: https://github.com/hao-ai-lab/JacobiForcing
==================================
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β https://t.me/DataScienceT
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π Summary:
Jacobi Forcing is a progressive distillation method that enables efficient parallel decoding of transformer-based models while maintaining performance, significantly reducing inference latency. AI-gen...
πΉ Publication Date: Published on Dec 16
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.14681
β’ PDF: https://arxiv.org/pdf/2512.14681
β’ Github: https://github.com/hao-ai-lab/JacobiForcing
==================================
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β https://t.me/DataScienceT
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β¨DiffusionVL: Translating Any Autoregressive Models into Diffusion Vision Language Models
π Summary:
DiffusionVL, a family of diffusion vision language models derived from autoregressive models through fine-tuning, achieves performance improvements and faster inference speeds compared to existing mod...
πΉ Publication Date: Published on Dec 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.15713
β’ PDF: https://arxiv.org/pdf/2512.15713
πΉ Models citing this paper:
β’ https://huggingface.co/hustvl/DiffusionVL-Qwen2.5VL-3B
β’ https://huggingface.co/hustvl/DiffusionVL-Qwen2.5VL-7B
==================================
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β https://t.me/DataScienceT
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π Summary:
DiffusionVL, a family of diffusion vision language models derived from autoregressive models through fine-tuning, achieves performance improvements and faster inference speeds compared to existing mod...
πΉ Publication Date: Published on Dec 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.15713
β’ PDF: https://arxiv.org/pdf/2512.15713
πΉ Models citing this paper:
β’ https://huggingface.co/hustvl/DiffusionVL-Qwen2.5VL-3B
β’ https://huggingface.co/hustvl/DiffusionVL-Qwen2.5VL-7B
==================================
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β https://t.me/DataScienceT
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β¨Qwen-Image-Layered: Towards Inherent Editability via Layer Decomposition
π Summary:
Qwen-Image-Layered decomposes images into semantically disentangled RGBA layers using a diffusion model, enabling independent editing of each layer and improving decomposition quality and consistency....
πΉ Publication Date: Published on Dec 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.15603
β’ PDF: https://arxiv.org/pdf/2512.15603
==================================
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β https://t.me/DataScienceT
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π Summary:
Qwen-Image-Layered decomposes images into semantically disentangled RGBA layers using a diffusion model, enabling independent editing of each layer and improving decomposition quality and consistency....
πΉ Publication Date: Published on Dec 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.15603
β’ PDF: https://arxiv.org/pdf/2512.15603
==================================
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β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β¨Step-GUI Technical Report
π Summary:
A self-evolving training pipeline with the Calibrated Step Reward System and GUI-MCP protocol improve GUI automation efficiency, accuracy, and privacy in real-world scenarios. AI-generated summary Rec...
πΉ Publication Date: Published on Dec 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.15431
β’ PDF: https://arxiv.org/pdf/2512.15431
==================================
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β https://t.me/DataScienceT
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π Summary:
A self-evolving training pipeline with the Calibrated Step Reward System and GUI-MCP protocol improve GUI automation efficiency, accuracy, and privacy in real-world scenarios. AI-generated summary Rec...
πΉ Publication Date: Published on Dec 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.15431
β’ PDF: https://arxiv.org/pdf/2512.15431
==================================
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β https://t.me/DataScienceT
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β¨Robust and Calibrated Detection of Authentic Multimedia Content
π Summary:
A resynthesis framework enhances deepfake detection by verifying authenticity with low false positive rates and robustness against efficient adversaries, supporting multiple modalities. AI-generated s...
πΉ Publication Date: Published on Dec 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.15182
β’ PDF: https://arxiv.org/pdf/2512.15182
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
A resynthesis framework enhances deepfake detection by verifying authenticity with low false positive rates and robustness against efficient adversaries, supporting multiple modalities. AI-generated s...
πΉ Publication Date: Published on Dec 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.15182
β’ PDF: https://arxiv.org/pdf/2512.15182
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β¨Is Nano Banana Pro a Low-Level Vision All-Rounder? A Comprehensive Evaluation on 14 Tasks and 40 Datasets
π Summary:
Nano Banana Pro excels in subjective visual quality across low-level vision tasks without fine-tuning but struggles with traditional reference-based quantitative metrics due to generative model stocha...
πΉ Publication Date: Published on Dec 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.15110
β’ PDF: https://arxiv.org/pdf/2512.15110
β’ Project Page: https://lowlevelbanana.github.io/
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
Nano Banana Pro excels in subjective visual quality across low-level vision tasks without fine-tuning but struggles with traditional reference-based quantitative metrics due to generative model stocha...
πΉ Publication Date: Published on Dec 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.15110
β’ PDF: https://arxiv.org/pdf/2512.15110
β’ Project Page: https://lowlevelbanana.github.io/
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research