#DataScience #MachineLearning #DeepLearning #Python #AI #MLProjects #DataAnalysis #ExplainableAI #100DaysOfCode #TechEducation #MLInterviewPrep #NeuralNetworks #MathForML #Statistics #Coding #AIForEveryone #PythonForDataScience
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👍10❤2
✨VADER: Towards Causal Video Anomaly Understanding with Relation-Aware Large Language Models
📝 Summary:
VADER is an LLM framework enhancing video anomaly understanding. It integrates keyframe object relations and visual cues to provide detailed, causally grounded descriptions and robust question answering, advancing explainable anomaly analysis.
🔹 Publication Date: Published on Nov 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07299
• PDF: https://arxiv.org/pdf/2511.07299
==================================
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✓ https://xn--r1a.website/DataScienceT
#LLM #VideoAnalytics #AnomalyDetection #Causality #ExplainableAI
📝 Summary:
VADER is an LLM framework enhancing video anomaly understanding. It integrates keyframe object relations and visual cues to provide detailed, causally grounded descriptions and robust question answering, advancing explainable anomaly analysis.
🔹 Publication Date: Published on Nov 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07299
• PDF: https://arxiv.org/pdf/2511.07299
==================================
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✓ https://xn--r1a.website/DataScienceT
#LLM #VideoAnalytics #AnomalyDetection #Causality #ExplainableAI
✨Transformer Explainer: Interactive Learning of Text-Generative Models
📝 Summary:
Transformer Explainer is an interactive web tool for non-experts to understand the GPT-2 model. It allows real-time experimentation with user input, visualizing how internal components predict text. This broadens access to education about modern generative AI.
🔹 Publication Date: Published on Aug 8, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2408.04619
• PDF: https://arxiv.org/pdf/2408.04619
• Project Page: https://poloclub.github.io/transformer-explainer/
• Github: https://github.com/helblazer811/ManimML
==================================
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✓ https://xn--r1a.website/DataScienceT
#AI #GenerativeAI #Transformers #AIeducation #ExplainableAI
📝 Summary:
Transformer Explainer is an interactive web tool for non-experts to understand the GPT-2 model. It allows real-time experimentation with user input, visualizing how internal components predict text. This broadens access to education about modern generative AI.
🔹 Publication Date: Published on Aug 8, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2408.04619
• PDF: https://arxiv.org/pdf/2408.04619
• Project Page: https://poloclub.github.io/transformer-explainer/
• Github: https://github.com/helblazer811/ManimML
==================================
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✓ https://xn--r1a.website/DataScienceT
#AI #GenerativeAI #Transformers #AIeducation #ExplainableAI
❤🔥1👍1
✨Rethinking Saliency Maps: A Cognitive Human Aligned Taxonomy and Evaluation Framework for Explanations
📝 Summary:
This paper introduces the RFxG taxonomy to categorize saliency map explanations by reference-frame and granularity. It proposes novel faithfulness metrics to improve evaluation, aiming to align explanations with diverse user intent and human understanding.
🔹 Publication Date: Published on Nov 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13081
• PDF: https://arxiv.org/pdf/2511.13081
==================================
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✓ https://xn--r1a.website/DataScienceT
#ExplainableAI #SaliencyMaps #CognitiveScience #AIEvaluation #AIResearch
📝 Summary:
This paper introduces the RFxG taxonomy to categorize saliency map explanations by reference-frame and granularity. It proposes novel faithfulness metrics to improve evaluation, aiming to align explanations with diverse user intent and human understanding.
🔹 Publication Date: Published on Nov 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13081
• PDF: https://arxiv.org/pdf/2511.13081
==================================
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✓ https://xn--r1a.website/DataScienceT
#ExplainableAI #SaliencyMaps #CognitiveScience #AIEvaluation #AIResearch
✨Fidelity-Aware Recommendation Explanations via Stochastic Path Integration
📝 Summary:
SPINRec improves recommendation explanation fidelity by using stochastic path integration and baseline sampling, capturing both observed and unobserved interactions. It consistently outperforms prior methods, setting a new benchmark for faithful explainability in recommender systems.
🔹 Publication Date: Published on Nov 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.18047
• PDF: https://arxiv.org/pdf/2511.18047
==================================
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✓ https://xn--r1a.website/DataScienceT
#RecommenderSystems #ExplainableAI #MachineLearning #AI #DataScience
📝 Summary:
SPINRec improves recommendation explanation fidelity by using stochastic path integration and baseline sampling, capturing both observed and unobserved interactions. It consistently outperforms prior methods, setting a new benchmark for faithful explainability in recommender systems.
🔹 Publication Date: Published on Nov 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.18047
• PDF: https://arxiv.org/pdf/2511.18047
==================================
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✓ https://xn--r1a.website/DataScienceT
#RecommenderSystems #ExplainableAI #MachineLearning #AI #DataScience
✨REFLEX: Self-Refining Explainable Fact-Checking via Disentangling Truth into Style and Substance
📝 Summary:
REFLEX is a new fact-checking method that uses internal model knowledge to improve verdict accuracy and explanation quality. It disentangles truth into style and substance via adaptive activation signals, achieving state-of-the-art performance with minimal training data. This approach also shows ...
🔹 Publication Date: Published on Nov 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20233
• PDF: https://arxiv.org/pdf/2511.20233
==================================
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✓ https://xn--r1a.website/DataScienceT
#FactChecking #ExplainableAI #MachineLearning #AI #NLP
📝 Summary:
REFLEX is a new fact-checking method that uses internal model knowledge to improve verdict accuracy and explanation quality. It disentangles truth into style and substance via adaptive activation signals, achieving state-of-the-art performance with minimal training data. This approach also shows ...
🔹 Publication Date: Published on Nov 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20233
• PDF: https://arxiv.org/pdf/2511.20233
==================================
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✓ https://xn--r1a.website/DataScienceT
#FactChecking #ExplainableAI #MachineLearning #AI #NLP
✨Show me the evidence: Evaluating the role of evidence and natural language explanations in AI-supported fact-checking
📝 Summary:
This study found that non-expert users consistently relied on evidence to validate AI claims in fact-checking. While natural language explanations reduced evidence use, participants still turned to evidence if explanations seemed flawed or insufficient. Evidence is a key ingredient for evaluating...
🔹 Publication Date: Published on Jan 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11387
• PDF: https://arxiv.org/pdf/2601.11387
==================================
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✓ https://xn--r1a.website/DataScienceT
#AI #FactChecking #ExplainableAI #Evidence #InformationCredibility
📝 Summary:
This study found that non-expert users consistently relied on evidence to validate AI claims in fact-checking. While natural language explanations reduced evidence use, participants still turned to evidence if explanations seemed flawed or insufficient. Evidence is a key ingredient for evaluating...
🔹 Publication Date: Published on Jan 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11387
• PDF: https://arxiv.org/pdf/2601.11387
==================================
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✓ https://xn--r1a.website/DataScienceT
#AI #FactChecking #ExplainableAI #Evidence #InformationCredibility
✨Transformer Explainer: Interactive Learning of Text-Generative Models
📝 Summary:
Transformer Explainer is an interactive web tool enabling non-experts to understand GPT-2's internal workings. It visualizes how the model generates text in real-time based on user input. This improves public access to learning about modern generative AI.
🔹 Publication Date: Published on Aug 8, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2408.04619
• PDF: https://arxiv.org/pdf/2408.04619
• Project Page: https://poloclub.github.io/transformer-explainer/
• Github: https://github.com/helblazer811/ManimML
==================================
For more data science resources:
✓ https://xn--r1a.website/DataScienceT
#AI #ExplainableAI #LLM #DataVisualization #GenerativeAI
📝 Summary:
Transformer Explainer is an interactive web tool enabling non-experts to understand GPT-2's internal workings. It visualizes how the model generates text in real-time based on user input. This improves public access to learning about modern generative AI.
🔹 Publication Date: Published on Aug 8, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2408.04619
• PDF: https://arxiv.org/pdf/2408.04619
• Project Page: https://poloclub.github.io/transformer-explainer/
• Github: https://github.com/helblazer811/ManimML
==================================
For more data science resources:
✓ https://xn--r1a.website/DataScienceT
#AI #ExplainableAI #LLM #DataVisualization #GenerativeAI
❤1
✨Causal Concept Graphs in LLM Latent Space for Stepwise Reasoning
📝 Summary:
Causal Concept Graphs identify causal relationships between concepts in LLMs using sparse autoencoders and differentiable structure learning. This method significantly improves causal fidelity for multi-step reasoning over prior techniques, yielding sparse and stable graphs.
🔹 Publication Date: Published on Mar 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.10377
• PDF: https://arxiv.org/pdf/2603.10377
==================================
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✓ https://xn--r1a.website/DataScienceT
#CausalAI #LLMs #MachineLearning #GraphLearning #ExplainableAI
📝 Summary:
Causal Concept Graphs identify causal relationships between concepts in LLMs using sparse autoencoders and differentiable structure learning. This method significantly improves causal fidelity for multi-step reasoning over prior techniques, yielding sparse and stable graphs.
🔹 Publication Date: Published on Mar 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.10377
• PDF: https://arxiv.org/pdf/2603.10377
==================================
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#CausalAI #LLMs #MachineLearning #GraphLearning #ExplainableAI
✨Dr. SHAP-AV: Decoding Relative Modality Contributions via Shapley Attribution in Audio-Visual Speech Recognition
📝 Summary:
Dr. SHAP-AV uses Shapley values to analyze audio-visual speech recognition modality contributions. Findings show models shift toward visual under noise but maintain a persistent audio bias. This method serves as a key diagnostic tool for AVSR.
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12046
• PDF: https://arxiv.org/pdf/2603.12046
• Project Page: https://umbertocappellazzo.github.io/Dr-SHAP-AV/
• Github: https://github.com/umbertocappellazzo/Dr-SHAP-AV
==================================
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#AVSR #ShapleyValues #ExplainableAI #MultimodalAI #SpeechRecognition
📝 Summary:
Dr. SHAP-AV uses Shapley values to analyze audio-visual speech recognition modality contributions. Findings show models shift toward visual under noise but maintain a persistent audio bias. This method serves as a key diagnostic tool for AVSR.
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12046
• PDF: https://arxiv.org/pdf/2603.12046
• Project Page: https://umbertocappellazzo.github.io/Dr-SHAP-AV/
• Github: https://github.com/umbertocappellazzo/Dr-SHAP-AV
==================================
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#AVSR #ShapleyValues #ExplainableAI #MultimodalAI #SpeechRecognition
❤1
✨Anatomy of a Lie: A Multi-Stage Diagnostic Framework for Tracing Hallucinations in Vision-Language Models
📝 Summary:
Vision-Language Models (VLMs) frequently "hallucinate" - generate plausible yet factually incorrect statements - posing a critical barrier to their trustworthy deployment. In this work, we propose a n...
🔹 Publication Date: Published on Mar 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.15557
• PDF: https://arxiv.org/pdf/2603.15557
• Github: https://github.com/Lexiang-Xiong/CAD
==================================
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#VLM #AIHallucinations #TrustworthyAI #ExplainableAI #AIResearch
📝 Summary:
Vision-Language Models (VLMs) frequently "hallucinate" - generate plausible yet factually incorrect statements - posing a critical barrier to their trustworthy deployment. In this work, we propose a n...
🔹 Publication Date: Published on Mar 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.15557
• PDF: https://arxiv.org/pdf/2603.15557
• Github: https://github.com/Lexiang-Xiong/CAD
==================================
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#VLM #AIHallucinations #TrustworthyAI #ExplainableAI #AIResearch
✨Progressive Training for Explainable Citation-Grounded Dialogue: Reducing Hallucination to Zero in English-Hindi LLMs
📝 Summary:
XKD-Dial is a progressive training pipeline for explainable, bilingual English-Hindi knowledge-grounded dialogue. It achieves zero hallucination rates by using citation grounding and improves explainability through post-hoc analyses.
🔹 Publication Date: Published on Mar 19
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.18911
• PDF: https://arxiv.org/pdf/2603.18911
==================================
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✓ https://xn--r1a.website/DataScienceT
#LLMs #ExplainableAI #NaturalLanguageProcessing #AIResearch #HallucinationReduction
📝 Summary:
XKD-Dial is a progressive training pipeline for explainable, bilingual English-Hindi knowledge-grounded dialogue. It achieves zero hallucination rates by using citation grounding and improves explainability through post-hoc analyses.
🔹 Publication Date: Published on Mar 19
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.18911
• PDF: https://arxiv.org/pdf/2603.18911
==================================
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#LLMs #ExplainableAI #NaturalLanguageProcessing #AIResearch #HallucinationReduction
AI & ML Papers
Photo
🔥 Transformer Explainer: Interactive Learning of Text-Generative Models
📅 Published on Aug 8, 2024
🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2408.04619
• PDF: https://arxiv.org/pdf/2408.04619
• Project Page: https://poloclub.github.io/transformer-explainer/
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📢 By: https://xn--r1a.website/PaperNexus
#TransformerModels #GPT2Explained #NaturalLanguageProcessing #TextGenerationModels #ExplainableAI
💡 The paper introduces Transformer Explainer, an interactive visualization tool that helps non-experts understand the inner workings of the GPT-2 model. The problem addressed is that Transformers, despite being a revolutionary machine learning technology, are often opaque to those without extensive expertise. To tackle this issue, the authors developed a tool that provides a model overview and allows users to smoothly transition across different abstraction levels of mathematical operations and model structures.
The method used to create the tool involves integrating a live GPT-2 instance that runs locally in the user's browser, enabling users to experiment with their own input and observe in real-time how the internal components and parameters of the Transformer work together to predict the next tokens. This approach allows users to gain hands-on experience and intuition about complex Transformer concepts without requiring installation or special hardware.
The results of this work are a publicly available, open-sourced tool that broadens access to education on modern generative AI techniques. The tool is accessible at a provided website and a video demo is also available, showcasing the tool's capabilities. Overall, the paper contributes to making Transformers more accessible and understandable to a wider audience, including non-experts, by providing an interactive and intuitive learning experience.
📅 Published on Aug 8, 2024
🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2408.04619
• PDF: https://arxiv.org/pdf/2408.04619
• Project Page: https://poloclub.github.io/transformer-explainer/
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📢 By: https://xn--r1a.website/PaperNexus
#TransformerModels #GPT2Explained #NaturalLanguageProcessing #TextGenerationModels #ExplainableAI
GitHub
Hugging Face
The AI community building the future. Hugging Face has 458 repositories available. Follow their code on GitHub.