Electronic proceedings author index

A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z
Patrick Menz
  • ESANN 2021 - Investigating Intensity and Transversal Drift in Hyperspectral Imaging Data [Details]
  • ESANN 2022 - Federated learning vector quantization for dealing with drift between nodes [Details]
  • ESANN 2022 - From hyperspectral to multispectral sensing – from simulation to reality: A comprehensive approach for calibration model transfer [Details]
Stefan Menzel
  • ESANN 2010 - Finding correlations in multimodal data using decomposition approaches [Details]
Rosa Meo
  • ESANN 2008 - Classification of chestnuts with feature selection by noise resilient classifiers [Details]
Alberto Meola
  • ESANN 2023 - Hybrid modelling of dynamic anaerobic digestion process in full-scale with LSTM NN and BMP measurements [Details]
  • ESANN 2025 - Reinforcement learning-based control system for biogas plants in laboratory scale [Details]
Francesco Mercaldo
  • ESANN 2021 - Robust Malware Classification via Deep Graph Networks on Call Graph Topologies [Details]
Siegfried Mercelis
  • ESANN 2025 - Artificial Surrogate Model for Computational Fluid Dynamics [Details]
David Mercier
  • ESANN 2014 - A robust regularization path for the Doubly Regularized Support Vector Machine [Details]
David Meredith
  • ESANN 2019 - training networks separately on static and dynamic obstacles improves collision avoidance during indoor robot navigation [Details]
J.J. Merelo
  • ESANN 1994 - A comparison of neural networks, linear controllers, genetic algorithms and simulated annealing for real time control [Details]
Erzsebet Merenyi
  • ESANN 2004 - Forbidden Magnification? I. [Details]
  • ESANN 2006 - Data topology visualization for the Self-Organizing Map [Details]
  • ESANN 2006 - Weighted differential topographic function: a refinement of topographic function [Details]
  • ESANN 2008 - Machine learning approches and pattern recognition for spectral data [Details]
  • ESANN 2012 - Parallel neural hardware: the time is right [Details]
  • ESANN 2012 - Unmixing Hyperspectral Images with Fuzzy Supervised Self-Organizing Maps [Details]
  • ESANN 2012 - gNBXe -- a Reconfigurable Neuroprocessor for Various Types of Self-Organizing Maps [Details]
Erzsebet Merenyi
  • ESANN 2004 - Forbidden magnification? II. [Details]
E. Merényi
  • ESANN 1998 - Self-organizing ANNs for planetary surface composition research [Details]
  • ESANN 1999 - Estimating the intrinsic dimensionality of hyperspectral images [Details]
  • ESANN 1999 - The challenges in spectral image analysis: an introduction, and review of ANN approaches [Details]
Erzsébet Merényi
  • ESANN 2021 - A Parameterless t-SNE for Faithful Cluster Embeddings from Prototype-based Learning and CONN Similarity [Details]
  • ESANN 2023 - SOM-based Classification and a Novel Stopping Criterion for Astroparticle Applications [Details]
Enrique Mérida-Casermeiro
  • ESANN 2006 - Enhanced maxcut clustering with multivalued neural networks and functional annealing [Details]
D. Merkl
  • ESANN 1996 - Identification of gait patterns with self-organizing maps based on ground reaction force [Details]
  • ESANN 1998 - Finding structure in text archives [Details]
  • ESANN 2000 - Using Growing hierarchical self-organizing maps for document classification [Details]
C. Merkwirth
  • ESANN 2000 - Nonlinear prediction of spatio-temporal time series [Details]
Paul Merlin
  • ESANN 2007 - SOM+EOF for finding missing values [Details]
  • ESANN 2009 - A robust hybrid DHMM-MLP modelling of financial crises measured by the WhIMS [Details]
  • ESANN 2009 - X-SOM and L-SOM: a nested approach for missing value imputation [Details]
Laurent Mertens
  • ESANN 2025 - Enhancing Computer Vision with Knowledge: a Rummikub Case Study [Details]
Pierre Merveille
  • ESANN 2025 - Can MDS rival with t-SNE by using the symmetric Kullback-Leibler divergence\\ across neighborhoods as a pseudo-distance? [Details]
Diego Mesquita
  • ESANN 2016 - K-means for Datasets with Missing Attributes: Building Soft Constraints with Observed and Imputed Values [Details]
  • ESANN 2016 - Using Robust Extreme Learning Machines to Predict Cotton Yarn Strength and Hairiness [Details]
  • ESANN 2017 - A Robust Minimal Learning Machine based on the M-Estimator [Details]
Diego Mesquita
  • ESANN 2021 - Improving Graph Variational Autoencoders with Multi-Hop Simple Convolutions [Details]
Attila Mester
  • ESANN 2021 - Validating static call graph-based malware signatures using community detection methods [Details]
Kevin Mets
  • ESANN 2020 - Language Grounded Task-Adaptation in Reinforcement Learning [Details]
  • ESANN 2024 - Online Adaptation of Compressed Models by Pre-Training and Task-Relevant Pruning [Details]
  • ESANN 2025 - Reducing the stability gap for continual learning at the edge with class balancing [Details]
G. Metta
  • ESANN 2000 - Learning VOR-like stabilization reflexes in robots [Details]
David Meunier
  • ESANN 2006 - Cluster detection algorithm in neural networks [Details]
  • ESANN 2008 - Neural networks for computational neuroscience [Details]
M. Meurer
  • ESANN 1996 - Towards constructive and destructive dynamic network configuration [Details]
Alfred Meurs
  • ESANN 2020 - Predicting low gamma- from lower frequency band activity in electrocorticography [Details]
A. Meyer-Bäse
  • ESANN 2003 - Model-Free Functional MRI Analysis Using Topographic Independent Component Analysis [Details]
Felix Meyer-Veit
  • No papers found
Felix Meyer-Veit
  • ESANN 2022 - Hyperspectral Wavelength Analysis with U-Net for Larynx Cancer Detection [Details]