Alex Aussem - RankMe

63 papers A* 3B 7C 2Misc 2Journal 25Unranked 23

Year	Rank	Type	Title / Venue / Authors
2026	—	`conf`	Lightweight Domain-Specific Language Model for Real-Time Structuring of Medical Prescriptions. EACL (Industry Track) Jonathan Pattin Cottet, Véronique Eglin, Alex Aussem
2023	Misc	`conf`	Answering Student Queries with a Supervised Memory Conversational Agent. FLAIRS Florian Baud, Alex Aussem
2023	Misc	`conf`	Non-Parametric Memory Guidance for Multi-Document Summarization. RANLP Florian Baud, Alex Aussem
2023	J	`jnl`	Non-Parametric Memory Guidance for Multi-Document Summarization. CoRR Florian Baud, Alex Aussem
2021	—	`conf`	Data-Efficient Information Extraction from Documents with Pre-trained Language Models. ICDAR Workshops (2) Clément Sage, Thibault Douzon, Alex Aussem, Véronique Eglin, Haytham Elghazel, Stefan Duffner, Christophe Garcia, Jérémy Espinas
2020	—	`conf`	End-to-End Extraction of Structured Information from Business Documents with Pointer-Generator Networks. SPNLP@EMNLP Clément Sage, Alex Aussem, Véronique Eglin, Haytham Elghazel, Jérémy Espinas
2019	B	`conf`	Hierarchical Recurrent Attention Networks for Context-Aware Education Chatbots. IJCNN Jean-Baptiste Aujogue, Alex Aussem
2018	—	`conf`	On the use of binary stochastic autoencoders for multi-label classification under the zero-one loss. INNS Conference on Big Data Denis Lecoeuche, Alex Aussem, Maxime Gasse
2017	—	`conf`	Dynamic Ensemble Selection with Probabilistic Classifier Chains. ECML/PKDD (1) Anil Narassiguin, Haytham Elghazel, Alex Aussem
2017	B	`conf`	Reducing variance due to importance weighting in covariate shift bias correction. ESANN Van-Tinh Tran, Alex Aussem
2016	J	`jnl`	An extensive empirical comparison of ensemble learning methods for binary classification. Pattern Anal. Appl. Anil Narassiguin, Mohamed Bibimoune, Haytham Elghazel, Alex Aussem
2016	J	`jnl`	Ensemble multi-label text categorization based on rotation forest and latent semantic indexing. Expert Syst. Appl. Haytham Elghazel, Alex Aussem, Ouadie Gharroudi, Wafa Saadaoui
2016	—	`conf`	F-Measure Maximization in Multi-Label Classification with Conditionally Independent Label Subsets. ECML/PKDD (1) Maxime Gasse, Alex Aussem
2016	J	`jnl`	F-measure Maximization in Multi-Label Classification with Conditionally Independent Label Subsets. CoRR Maxime Gasse, Alex Aussem
2016	—	`conf`	Identifying the irreducible disjoint factors of a multivariate probability distribution. Probabilistic Graphical Models Maxime Gasse, Alex Aussem
2016	—	`conf`	Similarity Tree Pruning: A Novel Dynamic Ensemble Selection Approach. ICDM Workshops Anil Narassiguin, Haytham Elghazel, Alex Aussem
2015	—	`conf`	A Practical Approach to Reduce the Learning Bias Under Covariate Shift. ECML/PKDD (2) Van-Tinh Tran, Alex Aussem
2015	J	`jnl`	A hybrid algorithm for Bayesian network structure learning with application to multi-label learning. CoRR Maxime Gasse, Alex Aussem, Haytham Elghazel
2015	J	`jnl`	An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning. CoRR Maxime Gasse, Alex Aussem, Haytham Elghazel
2015	—	`conf`	Calibrated k-labelsets for Ensemble Multi-label Classification. ICONIP (1) Ouadie Gharroudi, Haytham Elghazel, Alex Aussem
2015	—	`conf`	Correcting a Class of Complete Selection Bias with External Data Based on Importance Weight Estimation. ICONIP (3) Van-Tinh Tran, Alex Aussem
2015	B	`conf`	Ensemble Multi-label Classification: A Comparative Study on Threshold Selection and Voting Methods. ICTAI Ouadie Gharroudi, Haytham Elghazel, Alex Aussem
2015	J	`jnl`	Unsupervised feature selection with ensemble learning. Mach. Learn. Haytham Elghazel, Alex Aussem
2014	—	`conf`	A Comparison of Multi-Label Feature Selection Methods Using the Random Forest Paradigm. Canadian AI Ouadie Gharroudi, Haytham Elghazel, Alex Aussem
2014	J	`jnl`	A hybrid algorithm for Bayesian network structure learning with application to multi-label learning. Expert Syst. Appl. Maxime Gasse, Alex Aussem, Haytham Elghazel
2014	—	`conf`	Analysis of risk factors of hip fracture with causal Bayesian networks. IWBBIO Alex Aussem, Pascal Caillet, Zara Klemm, Maxime Gasse, Anne-Marie Schott, Michel Ducher
2013	J	`jnl`	Learning the local Bayesian network structure around the ZNF217 oncogene in breast tumours. Comput. Biol. Medicine Emmanuel Prestat, Sergio Rodrigues de Morais, Julie A. Vendrell, Aurélie Thollet, Christian Gautier, Pascale A. Cohen, Alex Aussem
2012	J	`jnl`	A semi-supervised feature ranking method with ensemble learning. Pattern Recognit. Lett. Fazia Bellal, Haytham Elghazel, Alex Aussem
2012	—	`conf`	An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning. ECML/PKDD (1) Maxime Gasse, Alex Aussem, Haytham Elghazel
2012	J	`jnl`	Analysis of nasopharyngeal carcinoma risk factors with Bayesian networks. Artif. Intell. Medicine Alex Aussem, Sergio Rodrigues de Morais, Marilys Corbex
2011	A*	`conf`	Semi-supervised Feature Importance Evaluation with Ensemble Learning. ICDM Hasna Barkia, Haytham Elghazel, Alex Aussem
2011	—	`ch.`	Trading-Off Diversity and Accuracy for Optimal Ensemble Tree Selection in Random Forests. Ensembles in Machine Learning Applications Haytham Elghazel, Alex Aussem, Florence Perraud
2010	J	`jnl`	A conservative feature subset selection algorithm with missing data. Neurocomputing Alex Aussem, Sergio Rodrigues de Morais
2010	J	`jnl`	A novel Markov boundary based feature subset selection algorithm. Neurocomputing Sergio Rodrigues de Morais, Alex Aussem
2010	—	`conf`	An Efficient and Scalable Algorithm for Local Bayesian Network Structure Discovery. ECML/PKDD (3) Sergio Rodrigues de Morais, Alex Aussem
2010	J	`jnl`	Analysis of lifestyle and metabolic predictors of visceral obesity with Bayesian Networks. BMC Bioinform. Alex Aussem, André Tchernof, Sergio Rodrigues de Morais, Sophie Rome
2010	J	`jnl`	Bayesian networks. Neurocomputing Alex Aussem
2010	A*	`conf`	Feature Selection for Unsupervised Learning Using Random Cluster Ensembles. ICDM Haytham Elghazel, Alex Aussem
2009	B	`conf`	Exploiting Data Missingness in Bayesian Network Modeling. IDA Sergio Rodrigues de Morais, Alex Aussem
2009	—	`conf`	Graph-Based Analysis of Nasopharyngeal Carcinoma with Bayesian Network Learning Methods. GbRPR Alex Aussem, Sergio Rodrigues de Morais, Marilys Corbex, Joël Favrel
2009	B	`conf`	Incremental Bayesian Network Learning for Scalable Feature Selection. IDA Grégory Thibault, Alex Aussem, Stéphane Bonnevay
2009	C	`conf`	Robust Gene Selection from Microarray Data with a Novel Markov Boundary Learning Method: Application to Diabetes Analysis. ECSQARU Alex Aussem, Sergio Rodrigues de Morais, Florence Perraud, Sophie Rome
2008	A*	`conf`	A Conservative Feature Subset Selection Algorithm with Missing Data. ICDM Alex Aussem, Sergio Rodrigues de Morais
2008	—	`conf`	A Novel Scalable and Data Efficient Feature Subset Selection Algorithm. ECML/PKDD (2) Sergio Rodrigues de Morais, Alex Aussem
2007	—	`conf`	A novel Bayesian Network structure learning algorithm based on minimal correlated itemset mining techniques. ICDIM Zahra Kebaili, Alex Aussem
2007	—	`conf`	Analysis of Nasopharyngeal Carcinoma Data with a Novel Bayesian Network Learning Algorithm. RIVF Alex Aussem, Sergio Rodrigues de Morais, Marilys Corbex
2007	B	`conf`	Nasopharyngeal Carcinoma Data Analysis with a Novel Bayesian Network Skeleton Learning Algorithm. AIME Alex Aussem, Sergio Rodrigues de Morais, Marilys Corbex
2003	C	`conf`	Closed Loop Stability of FIR-Recurrent Neural Networks. ICANN Alex Aussem
2003	J	`jnl`	Distributed Neural Networks for Quality of Service Estimation in Communication Networks. Int. J. Comput. Intell. Appl. Antoine Mahul, Alex Aussem
2002	J	`jnl`	Sufficient Conditions for Error Backflow Convergence in Dynamical Recurrent Neural Networks. Neural Comput. Alex Aussem
2002	—	`conf`	e-functional dependency inference: application to DNA microarray expression data. BDA Alex Aussem, Jean-Marc Petit
2001	J	`jnl`	Modeling the Ultimate Seaweed Expansion. Simul. David R. C. Hill, Patrick Coquillard, Alex Aussem, Jean de Vaugelas, Thierry Thibaut, Alexandre Meinesz
2001	B	`conf`	Segmentation of switching dynamics with a Hidden Markov Model of neural prediction experts. ESANN Alex Aussem, C. Boutevin
2001	J	`jnl`	Web traffic demand forecasting using wavelet-based multiscale decomposition. Int. J. Intell. Syst. Alex Aussem, Fionn Murtagh
2000	J	`jnl`	Neural network modelling for environmental prediction. Neurocomputing Fionn Murtagh, G. Zheng, Jonathan G. Campbell, Alex Aussem
2000	J	`jnl`	Neural-network metamodelling for the prediction of Caulerpa taxifolia development in the Mediterranean sea. Neurocomputing Alex Aussem, David R. C. Hill
2000	—	`conf`	Queuing Network Modeling with Distributed Neural Networks for Service Quality Estimation in B-ISDN Network. IJCNN (5) Alex Aussem, Antoine Mahul, Raymond Marie
2000	—	`conf`	Sufficient Conditions for Error Back Flow Convergence in Dynamical Recurrent Neural Networks. IJCNN (4) Alex Aussem
1999	J	`jnl`	Dynamical recurrent neural networks towards prediction and modeling of dynamical systems. Neurocomputing Alex Aussem
1998	—	`conf`	Data Imputation and Nowcasting in the Environmental Sciences Using Clustering and Connectionist Modelling. COMPSTAT Fionn Murtagh, G. Zheng, Jonathan G. Campbell, Alex Aussem, M. Ouberdous, E. Demirov, Walter Eifler, M. Crépon
1997	J	`jnl`	Combining Neural Network Forecasts on Wavelet-transformed Time Series. Connect. Sci. Alex Aussem, Fionn Murtagh
1996	J	`jnl`	Fuzzy astronomical seeing nowcasts with a dynamical and recurrent connectionist network. Neurocomputing Alex Aussem, Fionn Murtagh, Marc Sarazin
1995	J	`jnl`	Dynamical recurrent neural networks -- towards environmental time series prediction. Int. J. Neural Syst. Alex Aussem, Fionn Murtagh, Marc Sarazin