{"product_id":"braun-tanya-ki-2025-advances-in-artificial-intelligence-9783032028129","title":"KI 2025: Advances in Artificial Intelligence","description":".- Full Technical Papers.\n\u003cbr\u003e\n.- Augmenting Systematic Literature Reviews: A Human-AI CollaborativeFramework.\n\u003cbr\u003e\n.- Balanced Reciprocity for Data Sharing - Axiomatization andMechanism Design.\n\u003cbr\u003e\n.- Toward Short and Robust Contrastive Explanations for ImageClassification by Leveraging Instance Similarity and Concept Relevance.\n\u003cbr\u003e\n.- Intermediate-Task Transfer Learning for Bioacoustic Data.\n\u003cbr\u003e\n.- On the Domain Robustness of Contrastive Vision-Language Models.\n\u003cbr\u003e\n.- Numbers Don’t Lie: Hybrid Extraction and Validation of QuantitativeStatements in Arguments with Semi-Structured Information.\n\u003cbr\u003e\n.- A Hybrid Constraint-Based, Greedy, and Local Search Approach forthe Transshipment Problem.\n\u003cbr\u003e\n.- Re-examining learning linear functions in context.\n\u003cbr\u003e\n.- ODExAI: A Comprehensive Object Detection Explainable AI Evaluation.\n\u003cbr\u003e\n.- Enhancing Semi-Supervised Learning with a Meta-Feature BasedSafeguard System.\n\u003cbr\u003e\n.- Ca¨¿ssa AI: A Neuro-Symbolic Chess Agent for Explainable MoveSuggestion and Grounded Commentary.\n\u003cbr\u003e\n.- Unsupervised Selection of Features by their Resilience to the Curse ofDimensionality.\n\u003cbr\u003e\n.- Development of Hybrid Artificial Intelligence Training on Real andSynthetic Data.\n\u003cbr\u003e\n.- Towards Systematic Evaluation of Computer Vision Models under DataAnonymization.\n\u003cbr\u003e\n.- Accessible Language Simplification: Large Language Models forGenerating Easy German.\n\u003cbr\u003e\n.- Technical Communications.\n\u003cbr\u003e\n.- Learn, Optimize, Explain: A Neuro-Symbolic Advisor for Personal Finance.\n\u003cbr\u003e\n.- Deep learning emulators for large-scale, high-resolution urban pluvialflood prediction.\n\u003cbr\u003e\n.- Towards Observing the Effect of Abstraction on Understandability ofExplanations in Answer Set Programming.\n\u003cbr\u003e\n.- XAIRob — An Explainable-AI-Based Relative Robustness Measure forObject Detection.\n\u003cbr\u003e\n.- LLMs for Easy Language Translation: A Case Study on German PublicAuthorities Web Pages.\n\u003cbr\u003e\n.- Re-Evaluating the Robustness and Interpretability of the ContrastiveExplanations Method for Image Classification.\n\u003cbr\u003e\n.- Visualizing and Interpreting Neural Network Focus Regions: AComparative Study of Vision Transformers on Synthetic and Real Data.\n\u003cbr\u003e\n.- Comparing the visual quality of deep generative models for steelmicrostructures.\n\u003cbr\u003e\n.- Extended Abstracts.\n\u003cbr\u003e\n.- Makrut Attacks Against Black-Box Explanations.\n\u003cbr\u003e\n.- Positional Overload: Positional Debiasing and Context WindowExtension for Large Language Models using Set Encoding.\n\u003cbr\u003e\n.- Exploiting Contexts of LLM-based Code-Completion.\n\u003cbr\u003e\n.- The origins of AI research in the Federal Republic of Germany.\n\u003cbr\u003e\n.- Probabilities of the Third Type: Statistical Relational Learning andReasoning with Relative Frequencies.","brand":"Springer","offers":[{"title":"Default Title","offer_id":53752566939990,"sku":null,"price":0.0,"currency_code":"EUR","in_stock":false}],"url":"https:\/\/www.momoxbooks.com\/products\/braun-tanya-ki-2025-advances-in-artificial-intelligence-9783032028129","provider":"momoxbooks","version":"1.0","type":"link"}