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✨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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#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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#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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#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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#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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#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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#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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#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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#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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#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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#FactChecking #ExplainableAI #MachineLearning #AI #NLP