Ghent University Academic Bibliography

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FONDUE : framework for node disambiguation using network embeddings

Ahmad Mel (UGent) , Bo Kang (UGent) , Jefrey Lijffijt (UGent) and Tijl De Bie (UGent)

(2020) 2020 IEEE 7TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA 2020). In Proceedings of the International Conference on Data Science and Advanced Analytics p.158-167
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- Conference Paper
- P1
- open access
Benchmarking network embedding models for link prediction : are we making progress?

Alexandru Cristian Mara (UGent) , Jefrey Lijffijt (UGent) and Tijl De Bie (UGent)

(2020) 2020 IEEE 7TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA 2020). In Proceedings of the International Conference on Data Science and Advanced Analytics p.138-147
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- Miscellaneous
- open access
Network representation learning for link prediction : are we improving upon simple heuristics?

Alexandru Cristian Mara (UGent) , Jefrey Lijffijt (UGent) and Tijl De Bie (UGent)

(2020)
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Improved deep embedding learning based on stochastic symmetric triplet loss and local sampling

Bac Nguyen Cong (UGent) and Bernard De Baets (UGent)

(2020) NEUROCOMPUTING. 402. p.209-219
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Large-scale user modeling with recurrent neural networks for music discovery on multiple time scales

Cedric De Boom (UGent) , Rohan Agrawal, Samantha Hansen, Esh Kumar, Romain Yon, Ching-Wei Chen, Thomas Demeester (UGent) and Bart Dhoedt (UGent)

(2018) MULTIMEDIA TOOLS AND APPLICATIONS. 77(12). p.15385-15407
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Representation learning for very short texts using weighted word embedding aggregation

Cedric De Boom (UGent) , Steven Van Canneyt (UGent) , Thomas Demeester (UGent) and Bart Dhoedt (UGent)

(2016) PATTERN RECOGNITION LETTERS. 80. p.150-156
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- Conference Paper
- C1
- open access
Learning representations for tweets through word embeddings

Cedric De Boom (UGent) , Steven Van Canneyt (UGent) , Thomas Demeester (UGent) and Bart Dhoedt (UGent)

(2016) BENELEARN 2016 : proceedings of the 25th Belgian-Dutch conference on machine learning.

Academic Bibliography

FONDUE : framework for node disambiguation using network embeddings

Benchmarking network embedding models for link prediction : are we making progress?

Network representation learning for link prediction : are we improving upon simple heuristics?

Improved deep embedding learning based on stochastic symmetric triplet loss and local sampling

Large-scale user modeling with recurrent neural networks for music discovery on multiple time scales

Representation learning for very short texts using weighted word embedding aggregation

Learning representations for tweets through word embeddings

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