Zobrazeno 1 - 10
of 2 407
pro vyhledávání: '"James, P. F."'
Autor:
Karner, Clemens, Gröhl, Janek, Selby, Ian, Babar, Judith, Beckford, Jake, Else, Thomas R, Sadler, Timothy J, Shahipasand, Shahab, Thavakumar, Arthikkaa, Roberts, Michael, Rudd, James H. F., Schönlieb, Carola-Bibiane, Weir-McCall, Jonathan R, Breger, Anna
When developing machine learning models, image quality assessment (IQA) measures are a crucial component for evaluation. However, commonly used IQA measures have been primarily developed and optimized for natural images. In many specialized settings,
Externí odkaz:
http://arxiv.org/abs/2410.24098
We introduce a nested family of Bayesian nonparametric models for network and interaction data with a hierarchical granularity structure that naturally arises through finer and coarser population labelings. In the case of network data, the structure
Externí odkaz:
http://arxiv.org/abs/2408.04866
Modeling the trading volume curves of financial instruments throughout the day is of key interest in financial trading applications. Predictions of these so-called volume profiles guide trade execution strategies, for example, a common strategy is to
Externí odkaz:
http://arxiv.org/abs/2406.19402
Autor:
Wang, David G. L., Zhou, James Z. F.
We develop a composition method to unearth positive $e_I$-expansions of chromatic symmetric functions $X_G$, where the subscript $I$ stands for compositions rather than integer partitions. Using this method, we derive positive and neat $e_I$-expansio
Externí odkaz:
http://arxiv.org/abs/2401.01027
Autor:
Bao, Zhuoran, James, Daniel F. V.
It has been shown that the entanglement between the system state and the ancillary state is not a strict requirement for performing ancilla-assisted process tomography(AAPT). Instead, it only requires that the system-ancilla state be faithful, which,
Externí odkaz:
http://arxiv.org/abs/2312.14901
Whilst the size and complexity of ML models have rapidly and significantly increased over the past decade, the methods for assessing their performance have not kept pace. In particular, among the many potential performance metrics, the ML community s
Externí odkaz:
http://arxiv.org/abs/2312.16188
Autor:
Zhang, Fan, Kreuter, Daniel, Chen, Yichen, Dittmer, Sören, Tull, Samuel, Shadbahr, Tolou, Collaboration, BloodCounts!, Preller, Jacobus, Rudd, James H. F., Aston, John A. D., Schönlieb, Carola-Bibiane, Gleadall, Nicholas, Roberts, Michael
For healthcare datasets, it is often not possible to combine data samples from multiple sites due to ethical, privacy or logistical concerns. Federated learning allows for the utilisation of powerful machine learning algorithms without requiring the
Externí odkaz:
http://arxiv.org/abs/2310.02874
Autor:
Fatma A. M. Abdulsalam, Natalie E. Bourdakos, James W. F. Burns, Zoe Y. Zervides, Nathanael Q. E. Yap, Maamoun Adra, Hayato Nakanishi, Christian A. Than, Francis A. Chervenak, Sir Sabaratnam Arulkumaran
Publikováno v:
BMC Pregnancy and Childbirth, Vol 24, Iss 1, Pp 1-13 (2024)
Abstract Background Postpartum haemorrhage (PPH) continues to stand as the primary cause of maternal morbidity and mortality post-delivery, with twin pregnancies carrying a heightened risk of PPH compared to singleton deliveries. Objectives To invest
Externí odkaz:
https://doaj.org/article/4c8acff23adb4ba69bd1874a4e470c9f
Autor:
James, Lancelot F.
Gibbs type priors have been shown to be natural generalizations of Dirichlet process (DP) priors used for intricate applications of Bayesian nonparametric methods. This includes applications to mixture models and to species sampling models arising in
Externí odkaz:
http://arxiv.org/abs/2308.14254
Autor:
Dittmer, Sören, Roberts, Michael, Preller, Jacobus, COVNET, AIX, Rudd, James H. F., Aston, John A. D., Schönlieb, Carola-Bibiane
Survival analysis is an integral part of the statistical toolbox. However, while most domains of classical statistics have embraced deep learning, survival analysis only recently gained some minor attention from the deep learning community. This rece
Externí odkaz:
http://arxiv.org/abs/2307.13579