Results 41 to 50 of about 1,655 (147)
On the Complexity of Strong and Epistemic Credal Networks [PDF]
, 2013 Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013)
Mauá, Denis D. +3 more
openaire +3 more sources
Caring organizational cultures and the future of work
European Management Review, EarlyView.Abstract There is substantial evidence that workplaces of the future will be dominated by an increase in advanced technology. This trend might lead to the objectification and dehumanization of employees and other stakeholders who interact with organizations as impersonal operations and procedures become normative and employees are subordinated to ...
Alan M. Saks, Jamie A. Gruman
wiley +1 more source
A Recursive Algorithm for Computing Inferences in Imprecise Markov Chains
, 2019 We present an algorithm that can efficiently compute a broad class of inferences for discrete-time imprecise Markov chains, a generalised type of Markov chains that allows one to take into account partially specified probabilities and other types of ...
G Cooman de +5 more
core +1 more source
The Journal of American Culture, EarlyView.ABSTRACT Tim Burton's Christmas trilogy, Batman Returns, The Nightmare Before Christmas and Edward Scissorhands are all characterized by his trademark features. These include characters with ambiguous identities, apparently “normal” worlds adjacent to spaces associated with difference and exclusion, and the inevitable intrusion of the latter into the ...
Fran Pheasant‐Kelly
wiley +1 more source
Irrelevant natural extension for choice functions [PDF]
, 2019 We consider coherent choice functions under the recent axiomatisation proposed by De Bock and De Cooman that guarantees a representation in terms of binary preferences, and we discuss how to define conditioning in this framework.
Miranda, Enrique, Van Camp, Arthur
core
Uncertainty quantification is essential for deploying reliable Graph Neural Networks (GNNs), where existing approaches primarily rely on Bayesian inference or ensembles. In this paper, we introduce the first credal graph neural networks (CGNNs), which extend credal learning to the graph domain by training GNNs to output set-valued predictions in the ...
Tolloso, Matteo, Bacciu, Davide
openaire +2 more sources
Journal of Advanced Nursing, EarlyView.ABSTRACT Aim To identify the experiences and perceptions of healthcare professionals on artificial intelligence in healthcare. Design Systematic literature review of qualitative studies and meta‐aggregation. Data Sources CINAHL, PubMed, Scopus, Medic and ProQuest were systematically searched on December 9, 2024. Results Twenty‐six studies were included
Huotari Sini +6 more
wiley +1 more source
Exploiting Bayesian Network Sensitivity Functions for Inference in Credal Networks
, 2016 A Bayesian network is a concise representation of a joint probability distribution, which can be used to compute any probability of interest for the represented distribution. Credal networks were introduced to cope with the inevitable inaccuracies in the parametrisation of such a network.
Bolt, J.H., De Bock, Jasper, Renooij, S.
openaire +2 more sources
Journal of Economic Surveys, EarlyView.ABSTRACT We present the development and current state of the field of political economy. We implemented three bibliometric approaches (i.e., co‐citation, co‐word, and bibliographic coupling) and interpreted the results using the “invisible colleges” framework through six time frames (up until 1989, 1990–2001, 2002–2008, 2009–2016, 2017–2020, 2021–2023).
Jure Andolšek +2 more
wiley +1 more source
Evidential relational clustering using medoids [PDF]
, 2015 In real clustering applications, proximity data, in which only pairwise similarities or dissimilarities are known, is more general than object data, in which each pattern is described explicitly by a list of attributes. Medoid-based clustering algorithms,
Liu, Zhun-Ga +3 more
core +3 more sources