Zobrazeno 1 - 10
of 2 408
pro vyhledávání: '"SPATHIS, A"'
Autor:
Choi, Ryuhaerang, Chatterjee, Soumyajit, Spathis, Dimitris, Lee, Sung-Ju, Kawsar, Fahim, Malekzadeh, Mohammad
Developing new machine learning applications often requires the collection of new datasets. However, existing datasets may already contain relevant information to train models for new purposes. We propose SoundCollage: a framework to discover new cla
Externí odkaz:
http://arxiv.org/abs/2410.23008
Photoplethysmography (PPG) is the most widely used non-invasive technique for monitoring biosignals and cardiovascular health, with applications in both clinical settings and consumer health through wearable devices. Current machine learning models t
Externí odkaz:
http://arxiv.org/abs/2410.20542
Contrastive learning (CL) has emerged as a promising approach for representation learning in time series data by embedding similar pairs closely while distancing dissimilar ones. However, existing CL methods often introduce false negative pairs (FNPs
Externí odkaz:
http://arxiv.org/abs/2410.10048
Autor:
Yfantidou, Sofia, Spathis, Dimitris, Constantinides, Marios, Vakali, Athena, Quercia, Daniele, Kawsar, Fahim
Self-supervised learning (SSL) has become the de facto training paradigm of large models, where pre-training is followed by supervised fine-tuning using domain-specific data and labels. Despite demonstrating comparable performance with supervised met
Externí odkaz:
http://arxiv.org/abs/2406.02361
Autor:
Spathis, Dimitris, Saeed, Aaqib, Etemad, Ali, Tonekaboni, Sana, Laskaridis, Stefanos, Deldari, Shohreh, Tang, Chi Ian, Schwab, Patrick, Tailor, Shyam
This non-archival index is not complete, as some accepted papers chose to opt-out of inclusion. The list of all accepted papers is available on the workshop website.
Externí odkaz:
http://arxiv.org/abs/2403.10561
Wearable technologies enable continuous monitoring of various health metrics, such as physical activity, heart rate, sleep, and stress levels. A key challenge with wearable data is obtaining quality labels. Unlike modalities like video where the vide
Externí odkaz:
http://arxiv.org/abs/2401.14107
Autor:
Tang, Chi Ian, Qendro, Lorena, Spathis, Dimitris, Kawsar, Fahim, Mathur, Akhil, Mascolo, Cecilia
Wearable-based Human Activity Recognition (HAR) is a key task in human-centric machine learning due to its fundamental understanding of human behaviours. Due to the dynamic nature of human behaviours, continual learning promises HAR systems that are
Externí odkaz:
http://arxiv.org/abs/2401.02255
Autor:
Yfantidou, Sofia, Spathis, Dimitris, Constantinides, Marios, Vakali, Athena, Quercia, Daniele, Kawsar, Fahim
Self-supervised learning (SSL) has become the de facto training paradigm of large models where pre-training is followed by supervised fine-tuning using domain-specific data and labels. Hypothesizing that SSL models would learn more generic, hence les
Externí odkaz:
http://arxiv.org/abs/2401.01640
Autor:
Maria Anthi Kouri, Anastasios Georgopoulos, George E. Manios, Eirini Maratou, Aris Spathis, Sofia Chatziioannou, Kalliopi Platoni, Efstathios P. Efstathopoulos
Publikováno v:
Current Issues in Molecular Biology, Vol 46, Iss 11, Pp 12244-12259 (2024)
This study investigates a novel approach toward enhancing the efficacy of Lutetium-177 (Lu-177) radiopharmaceutical therapy by combining it with gold nanoparticles (AuNPs) in the HepG2 hepatic cancer cell line. Lu-177, known for its effective β radi
Externí odkaz:
https://doaj.org/article/4ad66ca9bc324fc1bf0e9335879bc020
Publikováno v:
Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing (UbiComp/ISWC '23 Adjunct )
How can we ensure that Ubiquitous Computing (UbiComp) research outcomes are both ethical and fair? While fairness in machine learning (ML) has gained traction in recent years, fairness in UbiComp remains unexplored. This workshop aims to discuss fair
Externí odkaz:
http://arxiv.org/abs/2309.12877