Martina Bacaro

An Adaptive Initial Design for Bayesian Optimization

Carolin Benjamins PhD at Leibniz University Hannover Our goal is to progress on Dynamic Algorithm Configuration (DAC) for Bayesian Optimization (BO). BO is a widely-used and sample-efficient framework for optimizing black-box problems, which are often expensive to evaluate. Dynamically configuring BO enables to adapt to the optimization progress and to any problem landscape without prior […]

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Leveraging Social Agents as Mediators to Foster Trust and Comprehension of Affective Engagement with Digital Content

Sergio Muñoz Assistant Professor at Universidad Politécnica de Madrid The vast and ever-expanding digital landscape presents significant challenges for users striving to navigate and discern accurate information. This challenge is compounded by the dynamic nature of the Internet, characterized by attention-seeking strategies intended to exploit users’ unconscious emotional responses. This affects the credibility of information

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Improving Multi-Task Parameter-Efficient Fine-Tuning Methods

Róbert Belanec PhD at Kempelen Institute of Intelligent Technologies The trustworthiness of the generative AI models is an important topic, especially with the increase in popularity of generative large language models. In recent years, the transformer architecture has become popular in the field of natural language processing. However, the increase in parameters is reducing the

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TAILOR selected papers: March

Every month, we want to acknowledge some valuable TAILOR papers, selected among the papers published by scientists belonging to our network by TAILOR principal investigator Fredrik Heintz. The list of the most valuable papers gathers contributions from different TAILOR partners, each providing valuable insights on different topics related to TrustworthyAI. Stay tuned for other valuable

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TAILOR selected papers: January

Every month, we want to acknowledge some valuable TAILOR papers, selected among the papers published by scientists belonging to our network by TAILOR principal investigator Fredrik Heintz. The list of the most valuable papers gathers contributions from different TAILOR partners, each providing valuable insights on different topics related to TrustworthyAI. Stay tuned for other valuable

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TAILOR scientists at AAAI2024

The 38th AAAI Conference on Artificial Intelligence (AAAI-24) will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February 2024. “The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the

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Evaluating the Effect of Additional Data Channels for CNN-based Radar Precipitation Nowcasting

Peter Pavlík PhD student at slovak.AI Abstract: Nowcasting in meteorology is defined as forecasting with high local detail, by any method, over a period from the present to six hours ahead. In general, the main nowcasting task is to predict precipitation amounts in the near future and generate alerts on extreme weather events, preventing damage

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Development of a neuro-symbolic AI approach to characterize diabetes distress profiles in people with type-1 diabetes

Dulce Canha PhD student at Luxembourg Institute of Health (LIH) Abstract: Type-1 diabetes (T1D) is an autoimmune disorder representing 5-10% of global diabetes cases, with a predicted patient growth from 8.4 million in 2021 to potentially 17.4 million by 2040. This chronic disease requires complex daily management, making people with T1D more prone to psychological

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Malware Detection Based on Explainable AI

Peter Anthony PhD student at Slovak.AI Comenius University in Bratislava, Slovakia Abstract: Malware detection is a critical task in cybersecurity, and traditional signature-based approaches are often ineffective against new and evolving threats. Recent research has shown that machine learning models can improve the accuracy of malware classification. However, existing methods often suffer from poor generalization

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