Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Qendro, Lorena"'
Traditional machine learning techniques are prone to generating inaccurate predictions when confronted with shifts in the distribution of data between the training and testing phases. This vulnerability can lead to severe consequences, especially in
Externí odkaz:
http://arxiv.org/abs/2402.09264
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:
Tang, Chi Ian, Qendro, Lorena, Spathis, Dimitris, Kawsar, Fahim, Mascolo, Cecilia, Mathur, Akhil
Publikováno v:
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 2841-2850
Self-supervised learning (SSL) has shown remarkable performance in computer vision tasks when trained offline. However, in a Continual Learning (CL) scenario where new data is introduced progressively, models still suffer from catastrophic forgetting
Externí odkaz:
http://arxiv.org/abs/2303.17235
Autor:
Tarkhani, Zahra, Qendro, Lorena, Brown, Malachy O'Connor, Hill, Oscar, Mascolo, Cecilia, Madhavapeddy, Anil
Brain computing interfaces (BCI) are used in a plethora of safety/privacy-critical applications, ranging from healthcare to smart communication and control. Wearable BCI setups typically involve a head-mounted sensor connected to a mobile device, com
Externí odkaz:
http://arxiv.org/abs/2201.07711
Electroencephalography (EEG) is crucial for the monitoring and diagnosis of brain disorders. However, EEG signals suffer from perturbations caused by non-cerebral artifacts limiting their efficacy. Current artifact detection pipelines are resource-hu
Externí odkaz:
http://arxiv.org/abs/2107.10746
Quantized neural networks (NN) are the common standard to efficiently deploy deep learning models on tiny hardware platforms. However, we notice that quantized NNs are as vulnerable to adversarial attacks as the full-precision models. With the prolif
Externí odkaz:
http://arxiv.org/abs/2105.06512
Recently, sound-based COVID-19 detection studies have shown great promise to achieve scalable and prompt digital pre-screening. However, there are still two unsolved issues hindering the practice. First, collected datasets for model training are ofte
Externí odkaz:
http://arxiv.org/abs/2104.02005
Neural networks (NNs) lack measures of "reliability" estimation that would enable reasoning over their predictions. Despite the vital importance, especially in areas of human well-being and health, state-of-the-art uncertainty estimation techniques a
Externí odkaz:
http://arxiv.org/abs/2102.05956
Publikováno v:
IEEE Journal of Biomedical and Health Informatics; November 2024, Vol. 28 Issue: 11 p6417-6428, 12p
Autor:
Qendro, Lorena
Sensor-equipped smartphones and wearables are transforming various mobile applications, including health monitoring. As the difference between consumer health wearables and medical devices begins to soften, it is now common for a single wearable devi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d2bf3b4477ac1b166a1dd640f6316157