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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Sleep states Challenge

In this Data Challenge, participants are tasked with developing machine learning models to accurately predict sleep states using Electroencephalography (EEG) data collected from IDUN Guardian Earbuds. This Data Challenge addresses the growing need for accessible, consumer-grade Brain Computer Interfaces (BCI) devices capable of providing reliable sleep monitoring and analysis. Electroencephalography (EEG) is a powerful, non-invasive

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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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Multitask learning for enhanced depressive symptoms screening using real-life voice recordings

Abir Elbeji Luxembourg Institute of Health (LIH) Abstract: Depression affects around 4.4% of the global population, requiring early and accurate screening to reduce its long-term damage. Traditional screening approaches such as self-reported questionnaires have limitations, prompting the shift to objective assessment methods such as voice-based biomarkers. These biomarkers offer non-invasive and scalable depression screening, though

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