.- PharML, Machine Learning for Pharma and Healthcare Applications.
.- CORKI: A Correlation-driven Imputation Method for Partial Annotation Scenarios in Multi-Label Clinical Problems.
.- Neuro-Symbolic Artificial Intelligence for Patient Monitoring.
.- Direct One-to-all Lead Conversion on 12-Lead Electrocardiogram.
.- Unveiling Driver Modules in Lung Cancer: A Clustering-Based Gene-Gene Interaction Network Analysis.
.- Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation.
.- Predicting Sepsis Onset with Deep Federated Learning.
.- A Workflow for Creating Multimodal Machine Learning Models for Metastasis Predictions in Melanoma Patients.
.- Molecular Fingerprints-based Machine Learning.
.- Simplification, Compression, Efficiency and Frugality for Artificial intelligence.
.- Neural Networks comprising Sequentially Semiseparable Matrices with one dimensional State Variable are Universal Approximators.
.- TinyMetaFed: Efficient Federated Meta-Learning for TinyML.
.- On The Potentials of Input Repetition in CNN Networks for Reducing Multiplications.
.- The Quest of Finding the Antidote to Sparse Double Descent.
.- Unveiling the Potential of Tiny Machine Learning for Enhanced People Counting in UWB Radar Data.
.- Towards Comparable Knowledge Distillation in Semantic Image Segmentation.
.- Combining Primal and Dual Representations in Deep Restricted Kernel Machines Classifiers.
.- Addressing limitations of TinyML approaches for AI-enabled Ambient Intelligence (Position Paper).
.- Leveraging low rank filters for efficient and knowledge-preserving lifelong learning.
.- Learning when to observe: A frugal reinforcement learning framework for a high-cost world.
.- Workshop on Uplift Modeling and Causal Machine Learning for Operational Decision Making.
.- Exploiting causal knowledge during CATE estimation using tree based metalearners.
.- A Parameter-Free Bayesian Framework for Uplift Modeling - Application on Telecom Data.
.- A churn prediction dataset from the telecom sector: a new benchmark for uplift modeling.
.- 6th Workshop on AI in Aging, Rehabilitation and Intelligent Assisted Living (ARIAL) .
.- Semi-Supervised Co-Teaching for Monitoring Parkinson's Disease Patients.
.- Explainable Artificial Intelligence in Medical Diagnostics: Insights into Alzheimer's Disease.
.- Cross-Modal Video to Body-joints Augmentation for Rehabilitation Exercise Quality Assessment.
.- Multimodal Sensor Fusion for Daily Living Activities Recognition in Active Assisted Living for Older Adults.
.- Modeling and Detecting Urinary Anomalies in Seniors from Data obtained by Unintrusive Sensors.
.- Assessing Frailty Using Behavioral and Physical Health Data in Everyday Living Settings.
.- Synthesizing Diabetic Foot Ulcer Images with Diffusion Model.
.- Engaging Older Adults at Meal-time through AI-empowered Socially Assistive Robots.
.- Investigating the Dynamics of Cardio-metabolic Comorbidities and their Interactions in Ageing Adults through Dynamic Bayesian Networks.
.- Adapting to Change: Reliable Multimodal Learning Across Domains.
.- Harnessing Error Patterns to Estimate Out-Of-Distribution Performance.
.- HAVE-Net: Hallucinated Audio-Visual Embeddings for Few-Shot Classification with Unimodal Cues.
.- CAD Models to Real-World Images: A Practical Approach to Unsupervised Domain Adaptation in Industrial Object Classification.
.- EMG subspace alignment and visualization for cross-subject hand gesture classification.
.- Adapting Classifiers To Changing Class Priors During Deployment.
.- AI4M: AI for Manufacturing.
.- Applying Machine Learning Models on Metrology Data for Predicting Device Electrical Performance.
.- Comparing Deep Reinforcement Learning Algorithms in Two-Echelon Supply Chains.
.- Reinforcement Learning for Segmented Manufacturing.
.- Automatic tool wear inspection by cascading sensor and image data.
