Results 41 to 50 of about 274,696 (183)

Statistical learning creates novel object associations via transitive relations [PDF]

Journal of Vision, 2016
A remarkable ability of the cognitive system is to make novel inferences on the basis of prior experiences. What mechanism supports such inferences? We propose that statistical learning is a process through which transitive inferences of new associations are made between objects that have never been directly associated.
Yu Luo, Jiaying Zhao
openaire   +3 more sources

Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly COVID-19 cases in Germany

Scientific Reports, 2022
During 2020, the infection rate of COVID-19 has been investigated by many scholars from different research fields. In this context, reliable and interpretable forecasts of disease incidents are a vital tool for policymakers to manage healthcare resources.
Cornelius Fritz, Emilio Dorigatti, David Rügamer   +2 more
doaj   +1 more source

Statistical relational learning for workflow mining

Intelligent Data Analysis, 2016
The management of business processes can support efficiency improvements in organizations. One of the most interesting problems is the mining and representation of process models in a declarative language. Various recently proposed knowledge-based languages showed advantages over graph-based procedural notations.
BELLODI, Elena, RIGUZZI, Fabrizio, LAMMA, Evelina   +2 more
openaire   +2 more sources

Lifted graphical models: a survey [PDF]

, 2015
Lifted graphical models provide a language for expressing dependencies between different types of entities, their attributes, and their diverse relations, as well as techniques for probabilistic reasoning in such multi-relational domains. In this survey,
Angelika Kimmig, B Ahmadi, B London, BL Richards, C Yanover, CM Bishop, D Heckerman, D Heckerman, D Koller, D Poole, D Suciu, EM Airoldi, FR Kschischang, H Khosravi, HR Lourenço, J Besag, J Neville, J Pearl, J Pearl, JR Quinlan, L Getoor, L Getoor, L Raedt De, L Raedt De, L Raedt De, L Raedt De, L Tierney, Lilyana Mihalkova, Lise Getoor, LR Rabiner, M Otterlo Van, M Richardson, MJ Wainwright, MP Wellman, N Lavrac̆, N Taghipour, O Schulte, O Schulte, P Damien, P Domingos, P Sen, P Spirtes, R Salvo Braz de, R Salvo Braz de, S Macskassy, S Muggleton, S Muggleton, S Natarajan   +47 more
core   +1 more source

Improving Data Quality by Leveraging Statistical Relational Learning [PDF]

, 2016
Digitally collected data su ↵ ers from many data quality issues, such as duplicate, incorrect, or incomplete data. A common approach for counteracting these issues is to formulate a set of data cleaning rules to identify and repair incorrect ...
Akbik, A, Kaul, Manohar, Markl, V, Rabl, T, Visengeriyeva, L   +4 more
core   +2 more sources

A Novel Hybrid Temporal Fusion Transformer Graph Neural Network Model for Stock Market Prediction

AppliedMath
Forecasting stock prices remains a central challenge in financial modelling, as markets are influenced by market sentiment, firm-level fundamentals and complex interactions between macroeconomic and microeconomic factors, for example.
Sebastian Thomas Lynch, Parisa Derakhshan, Stephen Lynch   +2 more
doaj   +1 more source

Composition of Sentence Embeddings: Lessons from Statistical Relational Learning [PDF]

Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019), 2019
Camera-ready for *SEM ...
Sileo, Damien, Van-De-Cruys, Tim, Pradel, Camille, Muller, Philippe   +3 more
openaire   +2 more sources

Idiomaticity Prediction of Chinese Noun Compounds and Its Applications

IEEE Access, 2019
Idiomaticity refers to the situation where the meaning of a lexical unit cannot be derived from the usual meanings of its constituents. As a ubiquitous phenomenon in languages, the existence of idioms often causes significant challenges for semantic NLP ...
Chengyu Wang, Yan Fan, Xiaofeng He, Hongyuan Zha, Aoying Zhou   +4 more
doaj   +1 more source

Structure Selection from Streaming Relational Data [PDF]

, 2011
Statistical relational learning techniques have been successfully applied in a wide range of relational domains. In most of these applications, the human designers capitalized on their background knowledge by following a trial-and-error trajectory, where
Mihalkova, Lilyana, Moustafa, Walaa Eldin   +1 more
core  

From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles

, 2017
The inference of network topologies from relational data is an important problem in data analysis. Exemplary applications include the reconstruction of social ties from data on human interactions, the inference of gene co-expression networks from DNA ...
A Fog, A Vidmer, B Karrer, C Aicher, D Liben-Nowell, G Robins, I Scholtes, J Jacod, JD Wilson, K Anand, M Domenico De, M Kivelä, M Molloy, M Rosvall, M Szell, MEJ Newman, MEJ Newman, N Eagle, N Eagle, P Erdös, P Holme, TP Peixoto, WW Zachary, Y Zhang   +23 more
core   +1 more source