Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Adel, Tameem"'
Publikováno v:
In Producing Artificial Intelligent Systems: The roles of Benchmarking, Standardisation and Certification, Studies in Computational Intelligence, edited by M. I. A. Ferreira, 2024, Springer
We review research at the National Physical Laboratory (NPL) in the area of trustworthy artificial intelligence (TAI), and more specifically trustworthy machine learning (TML), in the context of metrology, the science of measurement. We describe thre
Externí odkaz:
http://arxiv.org/abs/2406.10117
Both uncertainty estimation and interpretability are important factors for trustworthy machine learning systems. However, there is little work at the intersection of these two areas. We address this gap by proposing a novel method for interpreting un
Externí odkaz:
http://arxiv.org/abs/2006.06848
Approaches to continual learning aim to successfully learn a set of related tasks that arrive in an online manner. Recently, several frameworks have been developed which enable deep learning to be deployed in this learning scenario. A key modelling d
Externí odkaz:
http://arxiv.org/abs/1911.09514
We propose a novel algorithm for learning fair representations that can simultaneously mitigate two notions of disparity among different demographic subgroups in the classification setting. Two key components underpinning the design of our algorithm
Externí odkaz:
http://arxiv.org/abs/1910.07162
Autor:
Adel, Tameem1 (AUTHOR) tameem.adel@npl.co.uk, Levene, Mark1 (AUTHOR) mark.levene@npl.co.uk
Publikováno v:
Algorithms. Nov2023, Vol. 16 Issue 11, p526. 11p.
This article presents the prediction difference analysis method for visualizing the response of a deep neural network to a specific input. When classifying images, the method highlights areas in a given input image that provide evidence for or agains
Externí odkaz:
http://arxiv.org/abs/1702.04595
Autor:
Adel, Tameem, de Campos, Cassio P.
We present new algorithms for learning Bayesian networks from data with missing values using a data augmentation approach. An exact Bayesian network learning algorithm is obtained by recasting the problem into a standard Bayesian network learning pro
Externí odkaz:
http://arxiv.org/abs/1608.07734
Approximate Bayesian Computation (ABC) is a framework for performing likelihood-free posterior inference for simulation models. Stochastic Variational inference (SVI) is an appealing alternative to the inefficient sampling approaches commonly used in
Externí odkaz:
http://arxiv.org/abs/1606.08549
In this study, a spectral graph-theoretic grouping strategy for weakly supervised classification is introduced, where a limited number of labelled samples and a larger set of unlabelled samples are used to construct a larger annotated training set co
Externí odkaz:
http://arxiv.org/abs/1508.00507
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