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Cover image for Discovery Science 26th International Conference, DS 2023, Porto, Portugal, October 9-11, 2023, Proceedings
Title:
Discovery Science 26th International Conference, DS 2023, Porto, Portugal, October 9-11, 2023, Proceedings
Author:
Bifet, Albert. editor.
ISBN:
9783031452758
Edition:
1st ed. 2023.
Physical Description:
XXV, 721 p. 210 illus., 182 illus. in color. online resource.
Series:
Lecture Notes in Artificial Intelligence, 14276
Contents:
Ensembles of classifiers and quantifiers with data fusion for Quantification Learning -- Exploring the Intricacies of Neural Network Optimization -- Exploring the Reduction of Configuration Spaces of Workflows -- iSOUP-SymRF: Symbolic feature ranking with random forests in online multi-target regression -- Knowledge-Guided Additive Modeling For Supervised Regression -- Audience Prediction for Game Streaming Channels Based on Vectorization of User Comments -- From Tweets to Stance: An Unsupervised Framework for User Stance Detection on Twitter -- GLORIA: A Graph Convolutional Network-based Approach for Review Spam Detection -- Unmasking COVID-19 False Information on Twitter: a Topic-based Approach with BERT -- Unsupervised Key-Phrase Extraction from Long Texts with Multilingual Sentence Transformers -- Counterfactuals Explanations for Outliers via Subspaces Density Contrastive Loss -- Explainable Spatio-Temporal Graph Modeling -- ProbabilisticScoring Lists for Interpretable Machine Learning -- Refining Temporal Visualizations Using the Directional Coherence Loss -- Semantic enrichment of explanations of AI models for healthcare -- Text to Time Series Representations: Towards Interpretable Predictive Models -- Enhancing intra-modal similarity in a cross-modal triplet loss -- Exploring the Potential of Optimal Active Learning via a Non-myopic Oracle Policy -- Extrapolation is Not the Same as Interpolation -- Gene Interactions in Survival Data Analysis: A Data-driven Approach Using Restricted Mean Survival Time and Literature Mining -- Joining Imputation and Active Feature Acquisition for Cost Saving on Data Streams with Missing Features -- EXPHLOT: EXplainable Privacy assessment for Human LOcation Trajectories -- Fairness-aware Mixture of Experts with Interpretability Budgets -- GenFair: A Genetic Fairness-Enhancing Data Generation Framework -- Privacy-Preserving Learning of Random Forests Without Revealing the Trees -- Unlearning Spurious Correlations in Chest X-ray Classification -- Explaining the Chronological Attribution of Greek Papyri Images -- Leveraging the Spatiotemporal Analysis of Meisho-e Landscapes -- Predictive Inference Model of the Physical Environment that emulates Predictive Coding -- Transferring a Learned Qualitative Cart-Pole Control Model to Uneven Terrains -- Which Way to Go - Finding Frequent Trajectories Through Clustering -- Boosting-based Construction of BDDs for Linear Threshold Functions and Its Application to Verification of Neural Networks -- Interpretable Data Partitioning through Tree-based Clustering Methods -- Jaccard-constrained dense subgraph discovery -- RIMBO - an ontology for model revision databases -- Unsupervised Graph Neural Networks for Source Code Similarity Detection -- A Universal Approach for Post-Correcting Time Series -- Forecasts: Reducing Long-term ErrorsIn Multistep Scenarios -- Explainable Deep Learning-based Solar Flare Prediction with post hoc Attention for Operational Forecasting -- Pseudo Session-Based Recommendation with Hierarchical Embedding and Session Attributes -- Chance and the predictive limit in basketball (both college and professional) -- Exploring Label Correlations for Quantification of ICD Codes -- LGEM+: a first-order logic framework for automated improvement of metabolic network models through abduction -- Predicting age from human lung tissue through multi-modal data integration -- Error Analysis on Industry Data:Using Weak Segment Detection for Local Model Agnostic Prediction Intervals -- HEART: Heterogeneous Log Anomaly Detection using Robust Transformers -- Multi-Kernel Time Series Outlier Detection -- Toward Streamlining the Evaluation of Novelty Detection in Data Streams.
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