Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Jack K. Fitzsimons"'
Publikováno v:
ARES
The current COVID-19 pandemic highlights the utility of contact tracing, when combined with case isolation and social distancing, as an important tool for mitigating the spread of a disease [1]. Contact tracing provides a mechanism of identifying ind
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::45de79117d1f620b257da91f54006471
http://arxiv.org/abs/2004.05116
http://arxiv.org/abs/2004.05116
Autor:
Joseph F. Fitzsimons, Jack K. Fitzsimons, Stephen J. Roberts, Michael A. Osborne, Zhikuan Zhao
Publikováno v:
Physical Review A. 100
Gaussian processes (GPs) are important models in supervised machine learning. Training in Gaussian processes refers to selecting the covariance functions and the associated parameters in order to improve the outcome of predictions, the core of which
Publikováno v:
Entropy
Volume 21
Issue 8
Entropy, Vol 21, Iss 8, p 741 (2019)
Volume 21
Issue 8
Entropy, Vol 21, Iss 8, p 741 (2019)
Fairness, through its many forms and definitions, has become an important issue facing the machine learning community. In this work, we consider how to incorporate group fairness constraints in kernel regression methods, applicable to Gaussian proces
Autor:
Diego Granziol, Jack K. Fitzsimons, Stephen J. Roberts, Kurt Cutajar, Michael A. Osborne, Maurizio Filippone
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783319712482
ECML/PKDD (1)
ECML/PKDD (1)
The scalable calculation of matrix determinants has been a bottleneck to the widespread application of many machine learning methods such as determinantal point processes, Gaussian processes, generalised Markov random fields, graph models and many ot
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0ea663a7eb13202ae2f1c87a0fd77061
http://arxiv.org/abs/1704.07223
http://arxiv.org/abs/1704.07223
Gaussian processes (GP) are a widely used model for regression problems in supervised machine learning. Implementation of GP regression typically requires $O(n^3)$ logic gates. We show that the quantum linear systems algorithm [Harrow et al., Phys. R
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::375c979ee76fab0455a83f56cd122dce
Autor:
Thomas Lu, Jack K. Fitzsimons
Publikováno v:
SPIE Proceedings.
We present a novel technique for classifying static foreground in automated airport surveillance systems between abandoned and removed objects by representing the image as a Markov Random Field. The proposed algorithm computes and compares the net pr
Publikováno v:
International Journal of Pattern Recognition and Artificial Intelligence. 30:1655002
This paper extends the problem of abandoned/removed object classification in video surveillance to encompass the closely related (but to date ignored) problem of moved object classification. Existing abandoned/removed region classification techniques
Publikováno v:
Quantum Machine Intelligence, 3 (1)
Quantum Machine Intelligence
Quantum Machine Intelligence
Machine learning has recently emerged as a fruitful area for finding potential quantum computational advantage. Many of the quantum enhanced machine learning algorithms critically hinge upon the ability to efficiently produce states proportional to h