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ORCIDAlex Aussem
Alexandre Aussem
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2020 – today
- 2024
[j23]Guillaume Lefebvre
, Haytham Elghazel, Théodore Guillet, Alexandre Aussem, Matthieu Sonnati:
A new sentence embedding framework for the education and professional training domain with application to hierarchical multi-label text classification. Data Knowl. Eng. 150: 102281 (2024)- 2023
- [c58]Guillaume Lefebvre, Haytham Elghazel, Théodore Guillet, Alexandre Aussem, Matthieu Sonnati:
BERTEPro : Une nouvelle approche de représentation sémantique dans le domaine de l'éducation et de la formation professionnelle. EGC 2023: 211-222 - [c57]Miguel Palencia-Olivar, Stéphane Bonnevay, Alexandre Aussem, Bruno Canitia:
Topic modeling neuronal non-paramétrique pour l'extraction d'insight client : une application à l'industrie du pneumatique. EGC 2023: 499-506 - [c56]Florian Baud, Alexandre Aussem:
Répondre aux requêtes des étudiants avec un agent conversationnel à mémoire supervisée. EGC 2023: 631-632 - [c55]Florian Baud, Alex Aussem:
Answering Student Queries with a Supervised Memory Conversational Agent. FLAIRS 2023 - [c54]Florian Baud, Alex Aussem:
Non-Parametric Memory Guidance for Multi-Document Summarization. RANLP 2023: 153-158 - [c53]Guillaume Lefebvre, Haytham Elghazel, Théodore Guillet, Alexandre Aussem, Matthieu Sonnati:
BERTEPro : A new Sentence Embedding Framework for the Education and Professional Training domain. SAC 2023: 929-935 - [c52]Florian Baud, Alexandre Aussem:
Résumé automatique multi-documents guidé par une base de résumés similaires. CORIA-TALN (4) 2023: 19-27 - [i4]Florian Baud, Alex Aussem:
Non-Parametric Memory Guidance for Multi-Document Summarization. CoRR abs/2311.10760 (2023) - 2022
- [c51]Miguel Palencia-Olivar, Stéphane Bonnevay, Alexandre Aussem, Bruno Canitia:
Processus de Dirichlet profonds pour le topic modeling. EGC 2022: 355-362 - [c50]Miguel Palencia-Olivar, Stéphane Bonnevay, Alexandre Aussem, Bruno Canitia:
Nonparametric neural topic modeling for customer insight extraction about the tire industry. IJCNN 2022: 1-9 - 2021
- [c49]Clément Sage, Thibault Douzon, Alex Aussem, Véronique Eglin, Haytham Elghazel, Stefan Duffner, Christophe Garcia, Jérémy Espinas:
Data-Efficient Information Extraction from Documents with Pre-trained Language Models. ICDAR Workshops (2) 2021: 455-469 - [c48]Miguel Palencia-Olivar, Stéphane Bonnevay, Alexandre Aussem, Bruno Canitia:
Neural Embedded Dirichlet Processes for Topic Modeling. MDAI 2021: 299-310 - 2020
- [j22]Li Guo, Samia Boukir, Alexandre Aussem:
Building bagging on critical instances. Expert Syst. J. Knowl. Eng. 37(2) (2020) - [c47]Clément Sage, Alex Aussem, Véronique Eglin, Haytham Elghazel, Jérémy Espinas:
End-to-End Extraction of Structured Information from Business Documents with Pointer-Generator Networks. SPNLP@EMNLP 2020: 43-52
2010 – 2019
- 2019
- [c46]Clément Sage, Alexandre Aussem, Haytham Elghazel, Véronique Eglin, Jérémy Espinas:
Recurrent Neural Network Approach for Table Field Extraction in Business Documents. ICDAR 2019: 1308-1313 - [c45]Jean-Baptiste Aujogue, Alex Aussem:
Hierarchical Recurrent Attention Networks for Context-Aware Education Chatbots. IJCNN 2019: 1-8 - 2018
- [c44]Denis Lecoeuche, Alex Aussem, Maxime Gasse:
On the use of binary stochastic autoencoders for multi-label classification under the zero-one loss. INNS Conference on Big Data 2018: 71-80 - 2017
- [c43]Van-Tinh Tran, Alex Aussem:
Reducing variance due to importance weighting in covariate shift bias correction. ESANN 2017 - [c42]Anil Narassiguin, Haytham Elghazel, Alex Aussem:
Dynamic Ensemble Selection with Probabilistic Classifier Chains. ECML/PKDD (1) 2017: 169-186 - 2016
- [j21]Haytham Elghazel, Alex Aussem, Ouadie Gharroudi, Wafa Saadaoui:
Ensemble multi-label text categorization based on rotation forest and latent semantic indexing. Expert Syst. Appl. 57: 1-11 (2016) - [j20]Anil Narassiguin, Mohamed Bibimoune, Haytham Elghazel, Alex Aussem:
An extensive empirical comparison of ensemble learning methods for binary classification. Pattern Anal. Appl. 19(4): 1093-1128 (2016) - [c41]Ouadie Gharroudi, Haytham Elghazel, Alexandre Aussem:
A Semi-Supervised Ensemble Approach for Multi-label Learning. ICDM Workshops 2016: 1197-1204 - [c40]Anil Narassiguin, Haytham Elghazel, Alex Aussem:
Similarity Tree Pruning: A Novel Dynamic Ensemble Selection Approach. ICDM Workshops 2016: 1243-1250 - [c39]Maxime Gasse, Alex Aussem:
Identifying the irreducible disjoint factors of a multivariate probability distribution. Probabilistic Graphical Models 2016: 183-194 - [c38]Maxime Gasse, Alex Aussem:
F-Measure Maximization in Multi-Label Classification with Conditionally Independent Label Subsets. ECML/PKDD (1) 2016: 619-631 - [i3]Maxime Gasse, Alex Aussem:
F-measure Maximization in Multi-Label Classification with Conditionally Independent Label Subsets. CoRR abs/1604.07759 (2016) - 2015
- [j19]Haytham Elghazel, Alex Aussem:
Unsupervised feature selection with ensemble learning. Mach. Learn. 98(1-2): 157-180 (2015) - [c37]Maxime Gasse, Alexandre Aussem, Haytham Elghazel:
On the Optimality of Multi-Label Classification under Subset Zero-One Loss for Distributions Satisfying the Composition Property. ICML 2015: 2531-2539 - [c36]Van-Tinh Tran, Alex Aussem:
Correcting a Class of Complete Selection Bias with External Data Based on Importance Weight Estimation. ICONIP (3) 2015: 111-118 - [c35]Ouadie Gharroudi, Haytham Elghazel, Alex Aussem:
Calibrated k-labelsets for Ensemble Multi-label Classification. ICONIP (1) 2015: 573-582 - [c34]Ouadie Gharroudi, Haytham Elghazel, Alex Aussem:
Ensemble Multi-label Classification: A Comparative Study on Threshold Selection and Voting Methods. ICTAI 2015: 377-384 - [c33]Van-Tinh Tran, Alex Aussem:
A Practical Approach to Reduce the Learning Bias Under Covariate Shift. ECML/PKDD (2) 2015: 71-86 - [i2]Maxime Gasse, Alex Aussem, Haytham Elghazel:
An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning. CoRR abs/1505.05004 (2015) - [i1]Maxime Gasse, Alex Aussem, Haytham Elghazel:
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning. CoRR abs/1506.05692 (2015) - 2014
- [j18]Maxime Gasse, Alex Aussem, Haytham Elghazel:
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning. Expert Syst. Appl. 41(15): 6755-6772 (2014) - [c32]Ouadie Gharroudi, Haytham Elghazel, Alex Aussem:
A Comparison of Multi-Label Feature Selection Methods Using the Random Forest Paradigm. Canadian AI 2014: 95-106 - [c31]Alex Aussem, Pascal Caillet, Zara Klemm, Maxime Gasse, Anne-Marie Schott, Michel Ducher:
Analysis of risk factors of hip fracture with causal Bayesian networks. IWBBIO 2014: 1074-1085 - 2013
- [j17]Emmanuel Prestat, Sergio Rodrigues de Morais, Julie A. Vendrell, Aurélie Thollet, Christian Gautier, Pascale A. Cohen, Alex Aussem:
Learning the local Bayesian network structure around the ZNF217 oncogene in breast tumours. Comput. Biol. Medicine 43(4): 334-341 (2013) - 2012
- [j16]Alex Aussem, Sergio Rodrigues de Morais, Marilys Corbex:
Analysis of nasopharyngeal carcinoma risk factors with Bayesian networks. Artif. Intell. Medicine 54(1): 53-62 (2012) - [j15]Fazia Bellal, Haytham Elghazel, Alex Aussem:
A semi-supervised feature ranking method with ensemble learning. Pattern Recognit. Lett. 33(10): 1426-1432 (2012) - [c30]Maxime Gasse, Alex Aussem, Haytham Elghazel:
An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning. ECML/PKDD (1) 2012: 58-73 - 2011
- [c29]Hasna Barkia, Haytham Elghazel, Alex Aussem:
Semi-supervised Feature Importance Evaluation with Ensemble Learning. ICDM 2011: 31-40 - [p1]Haytham Elghazel, Alex Aussem, Florence Perraud:
Trading-Off Diversity and Accuracy for Optimal Ensemble Tree Selection in Random Forests. Ensembles in Machine Learning Applications 2011: 169-179 - 2010
- [j14]Alex Aussem, André Tchernof, Sergio Rodrigues de Morais, Sophie Rome:
Analysis of lifestyle and metabolic predictors of visceral obesity with Bayesian Networks. BMC Bioinform. 11: 487 (2010) - [j13]Alex Aussem:
Bayesian networks. Neurocomputing 73(4-6): 561-562 (2010) - [j12]Sergio Rodrigues de Morais, Alex Aussem:
A novel Markov boundary based feature subset selection algorithm. Neurocomputing 73(4-6): 578-584 (2010) - [j11]Alex Aussem, Sergio Rodrigues de Morais:
A conservative feature subset selection algorithm with missing data. Neurocomputing 73(4-6): 585-590 (2010) - [c28]Kais Allab, Khalid Benabdeslem, Alexandre Aussem:
Une approche de co-classification automatique à base des cartes topologiques. AAFD 2010: 1-24 - [c27]Haytham Elghazel, Alex Aussem:
Feature Selection for Unsupervised Learning Using Random Cluster Ensembles. ICDM 2010: 168-175 - [c26]Sergio Rodrigues de Morais, Alex Aussem:
An Efficient and Scalable Algorithm for Local Bayesian Network Structure Discovery. ECML/PKDD (3) 2010: 164-179
2000 – 2009
- 2009
- [c25]Alex Aussem, Sergio Rodrigues de Morais, Florence Perraud, Sophie Rome:
Robust Gene Selection from Microarray Data with a Novel Markov Boundary Learning Method: Application to Diabetes Analysis. ECSQARU 2009: 724-735 - [c24]Alex Aussem, Sergio Rodrigues de Morais, Marilys Corbex, Joël Favrel:
Graph-Based Analysis of Nasopharyngeal Carcinoma with Bayesian Network Learning Methods. GbRPR 2009: 52-61 - [c23]Sergio Rodrigues de Morais, Alex Aussem:
Exploiting Data Missingness in Bayesian Network Modeling. IDA 2009: 35-46 - [c22]Grégory Thibault, Alex Aussem, Stéphane Bonnevay:
Incremental Bayesian Network Learning for Scalable Feature Selection. IDA 2009: 202-212 - 2008
- [c21]Sergio Rodrigues de Morais, Alexandre Aussem, Marilys Corbex:
Handling almost-deterministic relationships in constraint-based Bayesian network discovery : Application to cancer risk factor identification. ESANN 2008: 101-106 - [c20]Fazia Bellal, Khalid Benabdeslem, Alexandre Aussem:
SOM based clustering with instance-level constraints. ESANN 2008: 313-318 - [c19]Anouar BenaHassena, Khalid Benabdeslem, Fazia Bellal, Alexandre Aussem, Bruno Canitia:
Intégration de contraintes dans les cartes auto-organisatrices. EGC 2008: 643-648 - [c18]Alex Aussem, Sergio Rodrigues de Morais:
A Conservative Feature Subset Selection Algorithm with Missing Data. ICDM 2008: 725-730 - [c17]Sergio Rodrigues de Morais, Alex Aussem:
A Novel Scalable and Data Efficient Feature Subset Selection Algorithm. ECML/PKDD (2) 2008: 298-312 - 2007
- [c16]Alex Aussem, Sergio Rodrigues de Morais, Marilys Corbex:
Nasopharyngeal Carcinoma Data Analysis with a Novel Bayesian Network Skeleton Learning Algorithm. AIME 2007: 326-330 - [c15]Alexandre Aussem, Sergio Rodrigues de Morais, Marilys Corbex:
Application des réseaux bayésiens à l'analyse des facteurs impliqués dans le cancer du Nasopharynx. EGC 2007: 123-134 - [c14]Khalid Benabdeslem, Mustapha Lebbah, Alexandre Aussem, Marilys Corbex:
Approche connexionniste pour l'extraction de profils cas-témoins du cancer du Nasopharynx à partir des données issues d'une étude épidémiologique. EGC 2007: 445-454 - [c13]Zahra Kebaili, Alex Aussem:
A novel Bayesian Network structure learning algorithm based on minimal correlated itemset mining techniques. ICDIM 2007: 121-126 - [c12]Grégory Thibault, Stéphane Bonnevay, Alexandre Aussem:
Learning Bayesian network structures by estimation of distribution algorithms: An experimental analysis. ICDIM 2007: 127-132 - [c11]Alex Aussem, Sergio Rodrigues de Morais, Marilys Corbex:
Analysis of Nasopharyngeal Carcinoma Data with a Novel Bayesian Network Learning Algorithm. RIVF 2007: 281-288 - 2006
- [c10]Alexandre Aussem, Pierre Chainais:
Modelling switching dynamics using prediction experts operating on distinct wavelet scales. ESANN 2006: 185-190 - [c9]Antoine Mahul, Alexandre Aussem:
Learning with monotonicity requirements for optimal routing with end-to-end quality of service constraints. ESANN 2006: 455-460 - [c8]Zahra Hamou Mamar, Pierre Chainais, Alexandre Aussem:
Probabilistic classifiers and time-scale representations: application to the monitoring of a tramway guiding system. ESANN 2006: 659-664 - [c7]Alexandre Aussem, Zahra Kebaili, Marilys Corbex, Fabien De Marchi:
Apprentissage de la structure des réseaux bayésiens à partir des motifs fréquents corrélés : application à l'identification des facteurs environnementaux du cancer du Nasopharynx. EGC 2006: 651-662 - 2003
- [j10]Antoine Mahul, Alex Aussem:
Distributed Neural Networks for Quality of Service Estimation in Communication Networks. Int. J. Comput. Intell. Appl. 3(3): 297-308 (2003) - [c6]Alex Aussem:
Closed Loop Stability of FIR-Recurrent Neural Networks. ICANN 2003: 523-529 - 2002
- [b1]Alexandre Aussem:
Le Calcul du Gradient d'Erreur dans les Réseaux de Neurones : Applications aux Telecom et aux Sciences Environnementales. Blaise Pascal University, Clermont-Ferrand, France, 2002 - [j9]Alex Aussem:
Sufficient Conditions for Error Backflow Convergence in Dynamical Recurrent Neural Networks. Neural Comput. 14(8): 1907-1927 (2002) - [c5]Alex Aussem, Jean-Marc Petit:
e-functional dependency inference: application to DNA microarray expression data. BDA 2002 - 2001
- [j8]Alex Aussem, Fionn Murtagh:
Web traffic demand forecasting using wavelet-based multiscale decomposition. Int. J. Intell. Syst. 16(2): 215-236 (2001) - [j7]David R. C. Hill, Patrick Coquillard, Alex Aussem, Jean de Vaugelas, Thierry Thibaut, Alexandre Meinesz:
Modeling the Ultimate Seaweed Expansion. Simul. 76(2): 126-134 (2001) - [c4]Alex Aussem, C. Boutevin:
Segmentation of switching dynamics with a Hidden Markov Model of neural prediction experts. ESANN 2001: 251-256 - 2000
- [j6]Fionn Murtagh, G. Zheng, Jonathan G. Campbell, Alex Aussem:
Neural network modelling for environmental prediction. Neurocomputing 30(1-4): 65-70 (2000) - [j5]Alex Aussem, David R. C. Hill:
Neural-network metamodelling for the prediction of Caulerpa taxifolia development in the Mediterranean sea. Neurocomputing 30(1-4): 71-78 (2000) - [c3]Alex Aussem, Antoine Mahul, Raymond Marie:
Queuing Network Modeling with Distributed Neural Networks for Service Quality Estimation in B-ISDN Network. IJCNN (5) 2000: 392-397 - [c2]Alex Aussem:
Sufficient Conditions for Error Back Flow Convergence in Dynamical Recurrent Neural Networks. IJCNN (4) 2000: 577-582
1990 – 1999
- 1999
- [j4]Alex Aussem:
Dynamical recurrent neural networks towards prediction and modeling of dynamical systems. Neurocomputing 28(1-3): 207-232 (1999) - 1998
- [c1]Fionn Murtagh, G. Zheng, Jonathan G. Campbell, Alex Aussem, M. Ouberdous, E. Demirov, Walter Eifler, M. Crépon:
Data Imputation and Nowcasting in the Environmental Sciences Using Clustering and Connectionist Modelling. COMPSTAT 1998: 401-406 - 1997
- [j3]Alex Aussem, Fionn Murtagh:
Combining Neural Network Forecasts on Wavelet-transformed Time Series. Connect. Sci. 9(1): 113-122 (1997) - 1996
- [j2]Alex Aussem, Fionn Murtagh, Marc Sarazin:
Fuzzy astronomical seeing nowcasts with a dynamical and recurrent connectionist network. Neurocomputing 13(2-4): 359-373 (1996) - 1995
- [j1]Alex Aussem, Fionn Murtagh, Marc Sarazin:
Dynamical recurrent neural networks -- towards environmental time series prediction. Int. J. Neural Syst. 6(2): 145-170 (1995)
Coauthor Index
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last updated on 2024-11-07 21:36 CET by the dblp team
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