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ORCIDLucas Drumond
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- affiliation: University of Hildesheim, Department of Computer Science
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2020 – today
- 2020
[i2]Armin Runge, Thomas Wenzel, Dimitrios Bariamis, Benedikt Sebastian Staffler, Lucas Rêgo Drumond, Michael Pfeiffer:
Bosch Deep Learning Hardware Benchmark. CoRR abs/2008.10293 (2020)
2010 – 2019
- 2018
- [c32]Omair Ghori, Radek Mackowiak, Miguel Ángel Bautista, Niklas Beuter, Lucas Drumond, Ferran Diego, Björn Ommer:
Learning to Forecast Pedestrian Intention from Pose Dynamics. Intelligent Vehicles Symposium 2018: 1277-1284 - 2017
- [c31]Hanh T. H. Nguyen, Martin Wistuba, Josif Grabocka, Lucas Rêgo Drumond, Lars Schmidt-Thieme
:
Personalized Deep Learning for Tag Recommendation. PAKDD (1) 2017: 186-197 - 2016
- [c30]Rosa Tsegaye Aga, Lucas Drumond, Christian Wartena, Lars Schmidt-Thieme:
Integrating Distributional and Lexical Information for Semantic Classification of Words using MRMF. COLING 2016: 2708-2717 - [c29]Rosa Tsegaye Aga, Christian Wartena, Lucas Drumond, Lars Schmidt-Thieme:
Learning Thesaurus Relations from Distributional Features. LREC 2016 - [c28]Nghia Duong-Trung, Nicolas Schilling, Lucas Drumond, Lars Schmidt-Thieme:
Matrix Factorization for Near Real-time Geolocation Prediction in Twitter Stream. LWDA 2016: 89-100 - [i1]Lucas Drumond, Ernesto Diaz-Aviles, Lars Schmidt-Thieme:
Multi-Relational Learning at Scale with ADMM. CoRR abs/1604.00647 (2016) - 2015
- [c27]Nicolas Schilling, Martin Wistuba, Lucas Drumond, Lars Schmidt-Thieme:
Joint Model Choice and Hyperparameter Optimization with Factorized Multilayer Perceptrons. ICTAI 2015: 72-79 - [c26]Nghia Duong-Trung, Martin Wistuba, Lucas Rêgo Drumond, Lars Schmidt-Thieme:
Geo_ML @ MediaEval Placing Task 2015. MediaEval 2015 - [c25]Nicolas Schilling, Martin Wistuba, Lucas Drumond, Lars Schmidt-Thieme:
Hyperparameter Optimization with Factorized Multilayer Perceptrons. ECML/PKDD (2) 2015: 87-103 - 2014
- [b1]Lucas Rêgo Drumond:
Factorization models for multi-relational data. University of Hildesheim, Cuvillier 2014, ISBN 978-3-95404-734-5, pp. 1-111 - [c24]Lucas Rêgo Drumond, Ernesto Diaz-Aviles, Lars Schmidt-Thieme, Wolfgang Nejdl:
Optimizing Multi-Relational Factorization Models for Multiple Target Relations. CIKM 2014: 191-200 - [c23]Lucas Rêgo Drumond, Lars Schmidt-Thieme, Christoph Freudenthaler, Artus Krohn-Grimberghe:
Collective Matrix Factorization of Predictors, Neighborhood and Targets for Semi-supervised Classification. PAKDD (1) 2014: 286-297 - 2013
- [c22]Josif Grabocka, Lucas Drumond, Lars Schmidt-Thieme:
Supervised Dimensionality Reduction via Nonlinear Target Estimation. DaWaK 2013: 172-183 - [c21]Ernesto Diaz-Aviles, Wolfgang Nejdl, Lucas Drumond, Lars Schmidt-Thieme:
Towards real-time collaborative filtering for big fast data. WWW (Companion Volume) 2013: 779-780 - 2012
- [c20]Ernesto Diaz-Aviles, Lucas Drumond, Zeno Gantner, Lars Schmidt-Thieme, Wolfgang Nejdl:
What is happening right now ... that interests me?: online topic discovery and recommendation in twitter. CIKM 2012: 1592-1596 - [c19]Ernesto Diaz-Aviles, Lucas Drumond, Lars Schmidt-Thieme, Wolfgang Nejdl:
Real-time top-n recommendation in social streams. RecSys 2012: 59-66 - [c18]Lucas Drumond, Steffen Rendle, Lars Schmidt-Thieme:
Predicting RDF triples in incomplete knowledge bases with tensor factorization. SAC 2012: 326-331 - [c17]Lucas Drumond, Nguyen Thai-Nghe, Tomás Horváth, Lars Schmidt-Thieme:
Factorization techniques for student performance classification and ranking. UMAP Workshops 2012 - [c16]Nguyen Thai-Nghe, Lucas Drumond, Tomás Horváth, Lars Schmidt-Thieme:
Using factorization machines for student modeling. UMAP Workshops 2012 - [c15]Artus Krohn-Grimberghe, Lucas Drumond, Christoph Freudenthaler, Lars Schmidt-Thieme:
Multi-relational matrix factorization using bayesian personalized ranking for social network data. WSDM 2012: 173-182 - [c14]Zeno Gantner, Lucas Drumond, Christoph Freudenthaler, Lars Schmidt-Thieme:
Personalized Ranking for Non-Uniformly Sampled Items. KDD Cup 2012: 231-247 - 2011
- [c13]Nguyen Thai-Nghe, Lucas Drumond, Tomás Horváth, Alexandros Nanopoulos, Lars Schmidt-Thieme:
Matrix and Tensor Factorization for Predicting Student Performance. CSEDU (1) 2011: 69-78 - [c12]Timo Reuter, Philipp Cimiano, Lucas Drumond, Krisztián Búza, Lars Schmidt-Thieme:
Scalable Event-Based Clustering of Social Media Via Record Linkage Techniques. ICWSM 2011 - 2010
- [c11]Zeno Gantner, Lucas Drumond, Christoph Freudenthaler, Steffen Rendle, Lars Schmidt-Thieme:
Learning Attribute-to-Feature Mappings for Cold-Start Recommendations. ICDM 2010: 176-185 - [c10]Lucas Drumond, Rosario Girardi:
Extracting ontology concept hierarchies from text using Markov logic. SAC 2010: 1354-1358 - [c9]Nguyen Thai-Nghe, Lucas Drumond, Artus Krohn-Grimberghe, Lars Schmidt-Thieme:
Recommender system for predicting student performance. RecSysTEL@RecSys 2010: 2811-2819
2000 – 2009
- 2009
- [c8]Lucas Drumond, Rosario Girardi, Djefferson Maranhão, Geraldo Abrantes:
Implementation Issues of the Infonorma Multi-agent Recommender System. ICEIS (4) 2009: 128-133 - [c7]Fabio Augusto de Santana Silva, Maria del Rosario Girardi, Lucas Rêgo Drumond:
An Information Retrieval Model for the Semantic Web. ITNG 2009: 143-148 - [c6]Fabio Silva, Rosario Girardi, Lucas Drumond:
A Knowledge-based Retrieval Model. SEKE 2009: 558-563 - 2008
- [j1]Lucas Drumond, Rosario Girardi:
A multi-agent legal recommender system. Artif. Intell. Law 16(2): 175-207 (2008) - [c5]Roberval Mariano, Rosario Girardi, Adriana Leite, Lucas Drumond, Djefferson Maranhão:
A Case Study on Domain Analysis of Semantic Web Multi-Agent Recommender Systems. ICSOFT (ISDM/ABF) 2008: 160-167 - [c4]Lucas Drumond, Rosario Girardi, Fabio Silva:
A Similarity Analysis Model for Semantic Web Information Filtering Applications. SEKE 2008: 638-642 - [c3]Lucas Drumond, Rosario Girardi:
A Survey of Ontology Learning Procedures. WONTO 2008 - 2007
- [c2]Lucas Drumond, Rosario Girardi, Adriana Leite:
Architectural design of a multi-agent recommender system for the legal domain. ICAIL 2007: 183-187 - [c1]Lucas Drumond, Rosario Girardi, Adriana Leite:
A Case Study on the Application of the MAAEM Methodology for the Specification Modeling of Recommender Systems in the Legal Domain. ICEIS (4) 2007: 155-160
Coauthor Index
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