Advances in Intelligent Data Analysis XIX

Modeling with Neural Networks.- Hyperspherical Weight Uncertainty in Neural Networks.- Partially Monotonic Learning for Neural Networks.- Multiple-Manifold Generation with an Ensemble GAN and Learned Noise Prior.- Simple, Efficient and Convenient Decentralized Multi-Task Learning for Neural Networks.- Deep Hybrid Neural Networks with Improved Weighted Word Embeddings for Sentiment Analysis.- Explaining Neural Networks by Decoding Layer Activations.- Analogical Embedding for Analogy-based Learning to Rank.- HORUS-NER: A Multimodal Named Entity Recognition Framework for Noisy Data.- Modeling with Statistical Learning.- Incremental Search Space Construction for Machine Learning Pipeline Synthesis.- Adversarial Vulnerability of Active Transfer Learning.- Revisiting Non-Specific Syndromic Surveillance.- Gradient Ascent for Best Response Regression.- Intelligent Structural Damage Detection: a Federated Learning Approach.- Composite surrogate for likelihood-freeBayesian optimisation in high-dimensional settings of activity-based transportation models.- Active Selection of Classification Features.- Feature Selection for Hierarchical Multi-Label Classification.- Bandit Algorithm for Both Unknown Best Position and Best Item Display on Web Pages.- Performance prediction for hardware-software configurations: A case study for video games.- avatar | Automated Feature Wrangling for Machine Learning.- Modeling Language and Graphs.- Semantically Enriching Embeddings of Highly In ectable Verbs for Improving Intent Detection in a Romanian Home Assistant Scenario.- BoneBert: A BERT-based Automated Information Extraction System of Radiology Reports for Bone Fracture Detection and Diagnosis.- Linking the Dynamics of User Stance to the Structure of Online Discussions.- Unsupervised Methods for the Study of Transformer Embeddings.- A Framework for Authorial Clustering of Shorter Texts in Latent Semantic Spaces.- DeepGG: a Deep Graph