Zobrazeno 1 - 10
of 7 477
pro vyhledávání: '"Imrie, A."'
Autor:
Imrie, Fergus, Denner, Stefan, Brunschwig, Lucas S., Maier-Hein, Klaus, van der Schaar, Mihaela
The application of machine learning in medicine and healthcare has led to the creation of numerous diagnostic and prognostic models. However, despite their success, current approaches generally issue predictions using data from a single modality. Thi
Externí odkaz:
http://arxiv.org/abs/2407.18227
Pseudo-labeling is a popular semi-supervised learning technique to leverage unlabeled data when labeled samples are scarce. The generation and selection of pseudo-labels heavily rely on labeled data. Existing approaches implicitly assume that the lab
Externí odkaz:
http://arxiv.org/abs/2406.13733
Empowering safe exploration of reinforcement learning (RL) agents during training is a critical impediment towards deploying RL agents in many real-world scenarios. Training RL agents in unknown, black-box environments poses an even greater safety ri
Externí odkaz:
http://arxiv.org/abs/2405.18180
Characterizing samples that are difficult to learn from is crucial to developing highly performant ML models. This has led to numerous Hardness Characterization Methods (HCMs) that aim to identify "hard" samples. However, there is a lack of consensus
Externí odkaz:
http://arxiv.org/abs/2403.04551
Autor:
Carwehl, Marc, Imrie, Calum, Vogel, Thomas, Rodrigues, Genaína, Calinescu, Radu, Grunske, Lars
In its quest for approaches to taming uncertainty in self-adaptive systems (SAS), the research community has largely focused on solutions that adapt the SAS architecture or behaviour in response to uncertainty. By comparison, solutions that reduce th
Externí odkaz:
http://arxiv.org/abs/2401.17187
Autor:
Feng, Nick, Marsso, Lina, Yaman, Sinem Getir, Baatartogtokh, Yesugen, Ayad, Reem, de Mello, Victória Oldemburgo, Townsend, Beverley, Standen, Isobel, Stefanakos, Ioannis, Imrie, Calum, Rodrigues, Genaína Nunes, Cavalcanti, Ana, Calinescu, Radu, Chechik, Marsha
As software systems increasingly interact with humans in application domains such as transportation and healthcare, they raise concerns related to the social, legal, ethical, empathetic, and cultural (SLEEC) norms and values of their stakeholders. No
Externí odkaz:
http://arxiv.org/abs/2401.05673
Autor:
Frauen, Dennis, Imrie, Fergus, Curth, Alicia, Melnychuk, Valentyn, Feuerriegel, Stefan, van der Schaar, Mihaela
Unobserved confounding is common in many applications, making causal inference from observational data challenging. As a remedy, causal sensitivity analysis is an important tool to draw causal conclusions under unobserved confounding with mathematica
Externí odkaz:
http://arxiv.org/abs/2311.16026
Evaluating the performance of machine learning models on diverse and underrepresented subgroups is essential for ensuring fairness and reliability in real-world applications. However, accurately assessing model performance becomes challenging due to
Externí odkaz:
http://arxiv.org/abs/2310.16524
Digital health tools have the potential to significantly improve the delivery of healthcare services. However, their adoption remains comparatively limited due, in part, to challenges surrounding usability and trust. Large Language Models (LLMs) have
Externí odkaz:
http://arxiv.org/abs/2310.03560
Autor:
Azhar Maqbool, Hema Viswambharan, Anna Skromna, Natallia Makava, Heba Shawer, Katherine Bridge, Shovkat Kadirovich Muminov, Helen Imrie, Kathryn Griffin, Stephen B Wheatcroft, Piruthivi Sukumar, Richard M Cubbon, Mark T Kearney, Nadira Yusupovna Yuldasheva
Publikováno v:
Vascular Biology, Vol 6, Iss 1, Pp 1-8 (2024)
Insulin resistance underpins the progression of type 2 diabetes mellitus and leads to a collection of risk factors for the development of atherosclerosis. Whether or not insulin resistance at a whole-body level per se leads to accelerated atheroscler
Externí odkaz:
https://doaj.org/article/04b8efb0aafa42a292c29ead35d01c2a
