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Book Synopsis

Applications of Data Science.- Credal Knowledge Tracing for Imprecise and Uncertain MCQ.- Paving the Way for the Prevention of Injuries in Outdoor Running.- Estimating the Learning Capacity of Bacterial Metabolic Networks.- Semi-supervised learning with pairwise instance comparisons for medical instance classification.- Local-global Data Augmentation for Contrastive Learning in Static
Sign Language Recognition.- SiamCircle: Trajectory Representation Learning in Free Settings.- Synthetic Tabular Data Detection In the Wild.- Assessing the Impact of Graph Structure Learning in Graph Deviation Networks.- Foundations of Data Science.- The When and How of Target Variable Transformations.- Transfer learning for balancing performance and scalability of ML models.- Balancing global importance and source proximity for personalized recommendations using random walk length.- Counterintuitive Behavior of Clustering Quality: Findings for K-Means
on Synthetic and Real Data.- BOWSA: a contribution of sensitivity analysis to improve Bayesian optimization for parameter tuning.- Overfitting in Combined Algorithm Selection and Hyperparameter
Optimization.- Local Subgroup Discovery on Attributed Network Graphs.- Imposing Constraints in Probabilistic Circuits via Gradient Optimization.- Natural Language Processing.- Improving Next Tokens via Second-Last Predictions with ’Generate and Refine’.- Detection of Large Language Model Contamination with Tabular Data.- Data Augmentation involving GMM and LLM.- Make Literature-Based Discovery Great Again through Reproducible Pipelines.- Extracting information in a low-resource setting: case study on bioinformatics workflows.- Vocabulary Quality in NLP Datasets: An Autoencoder-Based Framework Across Domains and Languages.- Temporal and Streaming Data Expertise Prediction of Tetris Players Using Eye Tracking Information.- Integrating Inverse and Forward Modeling for Sparse Temporal Data from Sensor Networks.- Bridging Spatial and Temporal Contexts: Sparse Transfer Learning.- Meta-learning and Data Augmentation for Stress Testing Forecasting Models.- Pragmatic Paradigm for Multi-stream Regression.- Two-in-one Models for Event Prediction and Time Series Forecasting. Comparison of Four Deep Learning Approaches to Simulate a Digital Patient under Anesthesia.- An Analysis of Temporal Dropout in Earth Observation Time Series for Regression Tasks.- Performative Drift Resistant Classification using Generative Domain Adversarial Networks.- Explainable and Interpretable Data Science.- Extracting Moore Machines from Transformers using Queries and Counterexamples.- Obtaining Example-Based Explanations from Deep Neural Networks.- Relevance-aware Algorithmic Recourse.- Expanding Polynomial Kernels for Global and Local Explanations of Support Vector Machines.- A Constrained Declarative Based Approach for Explainable Clustering.

Advances in Intelligent Data Analysis XXIII

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    A Paperback by Georg Krempl

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      View other formats and editions of Advances in Intelligent Data Analysis XXIII by Georg Krempl

      Publisher: Springer
      Publication Date: 07/06/2025
      ISBN13: 9783031913976, 978-3031913976
      ISBN10:
      Also in:
      Computing Databases / Data management

      Description

      Book Synopsis

      Applications of Data Science.- Credal Knowledge Tracing for Imprecise and Uncertain MCQ.- Paving the Way for the Prevention of Injuries in Outdoor Running.- Estimating the Learning Capacity of Bacterial Metabolic Networks.- Semi-supervised learning with pairwise instance comparisons for medical instance classification.- Local-global Data Augmentation for Contrastive Learning in Static
      Sign Language Recognition.- SiamCircle: Trajectory Representation Learning in Free Settings.- Synthetic Tabular Data Detection In the Wild.- Assessing the Impact of Graph Structure Learning in Graph Deviation Networks.- Foundations of Data Science.- The When and How of Target Variable Transformations.- Transfer learning for balancing performance and scalability of ML models.- Balancing global importance and source proximity for personalized recommendations using random walk length.- Counterintuitive Behavior of Clustering Quality: Findings for K-Means
      on Synthetic and Real Data.- BOWSA: a contribution of sensitivity analysis to improve Bayesian optimization for parameter tuning.- Overfitting in Combined Algorithm Selection and Hyperparameter
      Optimization.- Local Subgroup Discovery on Attributed Network Graphs.- Imposing Constraints in Probabilistic Circuits via Gradient Optimization.- Natural Language Processing.- Improving Next Tokens via Second-Last Predictions with ’Generate and Refine’.- Detection of Large Language Model Contamination with Tabular Data.- Data Augmentation involving GMM and LLM.- Make Literature-Based Discovery Great Again through Reproducible Pipelines.- Extracting information in a low-resource setting: case study on bioinformatics workflows.- Vocabulary Quality in NLP Datasets: An Autoencoder-Based Framework Across Domains and Languages.- Temporal and Streaming Data Expertise Prediction of Tetris Players Using Eye Tracking Information.- Integrating Inverse and Forward Modeling for Sparse Temporal Data from Sensor Networks.- Bridging Spatial and Temporal Contexts: Sparse Transfer Learning.- Meta-learning and Data Augmentation for Stress Testing Forecasting Models.- Pragmatic Paradigm for Multi-stream Regression.- Two-in-one Models for Event Prediction and Time Series Forecasting. Comparison of Four Deep Learning Approaches to Simulate a Digital Patient under Anesthesia.- An Analysis of Temporal Dropout in Earth Observation Time Series for Regression Tasks.- Performative Drift Resistant Classification using Generative Domain Adversarial Networks.- Explainable and Interpretable Data Science.- Extracting Moore Machines from Transformers using Queries and Counterexamples.- Obtaining Example-Based Explanations from Deep Neural Networks.- Relevance-aware Algorithmic Recourse.- Expanding Polynomial Kernels for Global and Local Explanations of Support Vector Machines.- A Constrained Declarative Based Approach for Explainable Clustering.

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