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Pedro Mercado
Pedro Mercado
Saarland University - University of Tübingen - AWS AI Labs, Amazon Web Services
Verified email at amazon.com - Homepage
Title
Cited by
Cited by
Year
Clustering signed networks with the geometric mean of Laplacians
P Mercado, F Tudisco, M Hein
NeurIPS 2016, 4421-4429, 2016
462016
Spectral Clustering of Signed Graphs via Matrix Power Means
P Mercado, F Tudisco, M Hein
ICML 2019, 2019
322019
The power mean Laplacian for multilayer graph clustering
P Mercado, A Gautier, F Tudisco, M Hein
AISTATS 2018, 2018
242018
End-to-end learning of coherent probabilistic forecasts for hierarchical time series
SS Rangapuram, LD Werner, K Benidis, P Mercado, J Gasthaus, ...
ICML 2021, 8832-8843, 2021
212021
Node classification for signed networks using diffuse interface methods
P Mercado, J Bosch, M Stoll
ECMLPKDD 2019, 2019
18*2019
Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs
P Mercado, F Tudisco, M Hein
NeurIPS 2019, 2019
142019
Community detection in networks via nonlinear modularity eigenvectors
F Tudisco, P Mercado, M Hein
SIAM Journal on Applied Mathematics 78 (5), 2393-2419, 2018
142018
Towards Lyrics Spotting in the SyncGlobal Project
C Dittmar, P Mercado, H Grossmann, E Cano
3rd International Workshop on Cognitive Information Processing (CIP), 2012
72012
PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series
J Paul, BS Michael, M Pedro, SN Rajbir, K Shubham, F Valentin, G Jan, ...
ICLR 2022, 2021
3*2021
Feature selection in clustering with constraints: Application to active exploration of music collections
P Mercado, H Lukashevich
ICMLA 2010, 649-654, 2010
32010
Applying constrained clustering for active exploration of music collections
P Mercado, H Lukashevich
1st Workshop on Music Recommendation and Discovery (WOMRAD), 39, 2010
32010
Beyond the arithmetic mean: extensions of spectral clustering and semi-supervised learning for signed and multilayer graphs via matrix power means
P Mercado
Saarländische Universitäts-und Landesbibliothek, 2021
2021
Graph-based PDEs: Laplacians, eigeninformation, and semi-supervised learning
J Bosch, S Klamt, P Mercado, M Stoll
XXI Householder Symposium on Numerical Linear Algebra, 84, 2020
2020
End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series Download PDF Open Website
SS Rangapuram, LD Werner, K Benidis, P Mercado, J Gasthaus, ...
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