Results 51 to 60 of about 21,280,601 (295)
Structure Learning via Parameter Learning [PDF]
Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, 2014 A key challenge in information and knowledge management is to automatically discover the underlying structures and patterns from large collections of extracted information. This paper presents a novel structure-learning method for a new, scalable probabilistic logic called ProPPR.
William Yang Wang +2 more
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Hypergraph Structure Learning for Hypergraph Neural Networks
International Joint Conference on Artificial Intelligence, 2022 Hypergraphs are natural and expressive modeling tools to encode high-order relationships among entities. Several variations of Hypergraph Neural Networks (HGNNs) are proposed to learn the node representations and complex relationships in the hypergraphs.
D. Cai +5 more
semanticscholar +1 more source
Machine-learning mathematical structures
International Journal of Data Science in the Mathematical Sciences, 2022 We review, for a general audience, a variety of recent experiments on extracting structure from machine-learning mathematical data that have been compiled over the years. Focusing on supervised machine-learning on labeled data from different fields ranging from geometry to representation theory, from combinatorics to number theory, we present a ...
openaire +2 more sources
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning [PDF]
, 2014 We present a novel hybrid algorithm for Bayesian network structure learning, called H2PC. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges.
Aussem, Alex +2 more
core +6 more sources
Journal of Statistical Software, 2017 It is well known in the literature that the problem of learning the structure of Bayesian networks is very hard to tackle: Its computational complexity is super-exponential in the number of nodes in the worst case and polynomial in most real-world ...
Marco Scutari
doaj +1 more source
IEEE Access, 2022 Learning the structure of Bayesian networks is a challenging problem because it is a NP-Hard problem. As an excellent search & score based method, the K2 algorithm strongly depends on the input of global order of all nodes to ensure the result is ...
Kunhua Zhong +3 more
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Social structure learning in human anterior insula
eLife, 2020 Humans form social coalitions in every society, yet we know little about how we learn and represent social group boundaries. Here we derive predictions from a computational model of latent structure learning to move beyond explicit category labels and ...
Tatiana Lau +2 more
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Behavioural Brain Research, 2010 'Learning to learn' phenomena have been widely investigated in cognition, perception and more recently also in action. During concept learning tasks, for example, it has been suggested that characteristic features are abstracted from a set of examples with the consequence that learning of similar tasks is facilitated-a process termed 'learning to learn'
Braun, Daniel A. +2 more
openaire +4 more sources
Person Re-identification with Correspondence Structure Learning [PDF]
, 2015 This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification. We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-
Lin, Weiyao +5 more
core +1 more source
Journal of Agricultural Sciences, 2020 The variables affecting the milk productivity have been discussed in various articles through different methods. A recent study using path analysis shows that three variables significantly affect the 305-day milk yield of Holstein Friesian cows ...
Volkan Sevinç +3 more
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