Zobrazeno 1 - 10
of 76
pro vyhledávání: '"Komeili, Majid"'
Autor:
Hobson, David G, Komeili, Majid
Blindness and visual impairments affect many people worldwide. For help with navigation, people with visual impairments often rely on tactile maps that utilize raised surfaces and edges to convey information through touch. Although these maps are hel
Externí odkaz:
http://arxiv.org/abs/2412.07191
The accurate recognition of symptoms in clinical reports is significantly important in the fields of healthcare and biomedical natural language processing. These entities serve as essential building blocks for clinical information extraction, enablin
Externí odkaz:
http://arxiv.org/abs/2401.15780
Part-prototype networks have recently become methods of interest as an interpretable alternative to many of the current black-box image classifiers. However, the interpretability of these methods from the perspective of human users has not been suffi
Externí odkaz:
http://arxiv.org/abs/2310.06966
Vaccine hesitancy continues to be a main challenge for public health officials during the COVID-19 pandemic. As this hesitancy undermines vaccine campaigns, many researchers have sought to identify its root causes, finding that the increasing volume
Externí odkaz:
http://arxiv.org/abs/2304.06858
Autor:
O'Shea, Galen, Komeili, Majid
Gaze tracking is a valuable tool with a broad range of applications in various fields, including medicine, psychology, virtual reality, marketing, and safety. Therefore, it is essential to have gaze tracking software that is cost-efficient and high-p
Externí odkaz:
http://arxiv.org/abs/2303.10151
Autor:
Zarei, Mohammad Reza, Komeili, Majid
Few-shot learning (FSL) is a challenging learning problem in which only a few samples are available for each class. Decision interpretation is more important in few-shot classification since there is a greater chance of error than in traditional clas
Externí odkaz:
http://arxiv.org/abs/2211.09107
Autor:
Zarei, Mohammad Reza, Komeili, Majid
Few-shot learning aims at recognizing new instances from classes with limited samples. This challenging task is usually alleviated by performing meta-learning on similar tasks. However, the resulting models are black-boxes. There has been growing con
Externí odkaz:
http://arxiv.org/abs/2202.13474
Autor:
Fraser, Kathleen C., Komeili, Majid
Publikováno v:
IEEE Instrumentation & Measurement Magazine (Volume: 24, Issue: 6, September 2021)
The population is aging, and becoming more tech-savvy. The United Nations predicts that by 2050, one in six people in the world will be over age 65 (up from one in 11 in 2019), and this increases to one in four in Europe and Northern America. Meanwhi
Externí odkaz:
http://arxiv.org/abs/2110.09421
Autor:
Davoodi, Omid, Komeili, Majid
Growing concerns regarding the operational usage of AI models in the real-world has caused a surge of interest in explaining AI models' decisions to humans. Reinforcement Learning is not an exception in this regard. In this work, we propose a method
Externí odkaz:
http://arxiv.org/abs/2105.07099