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
Lucas Schwarz
  • ESANN 2021 - The LVQ-based Counter Propagation Network -- an Interpretable Information Bottleneck Approach [Details]
Cornelius Schwarz
  • ESANN 2008 - Direct and inverse solution for a stimulus adaptation problem using SVR [Details]
H. Schwegler
  • ESANN 1995 - Predicting spike train responses of neuron models [Details]
  • ESANN 1999 - Critical and non-critical avalanche behavior in networks of integrate-and-fire neurons [Details]
  • ESANN 2001 - Extracting motion information using a biologically realistic model retina [Details]
Friedhelm Schwenker
  • ESANN 2020 - Automatic Pain Intensity Recognition: Training Set Selection based on Outliers and Centroids [Details]
  • ESANN 2023 - Potential analysis of a Quantum RL controller in the context of autonomous driving [Details]
Friedhelm Schwenker
  • ESANN 2004 - Classification of Bioacoustic Time Series by Training a Decision Fusion mapping [Details]
  • ESANN 2008 - Detecting zebra crossings utilizing AdaBoost [Details]
  • ESANN 2008 - Learning to play Tetris applying reinforcement learning methods [Details]
  • ESANN 2008 - Multi-View Forests of Tree-Structured Radial Basis Function Networks Based on Dempster-Shafer Evidence Theory [Details]
  • ESANN 2009 - Echo State networks and Neural network Ensembles to predict Sunspots activity [Details]
  • ESANN 2011 - Training of multiple classifier systems utilizing partially labeled sequential data sets [Details]
  • ESANN 2014 - Selective Neural Network Ensembles in Reinforcement Learning [Details]
  • ESANN 2015 - SMO Lattices for the Parallel Training of Support Vector Machines [Details]
  • ESANN 2017 - Hierarchical Combination of Video Features for Personalised Pain Level Recognition [Details]
P. Schyns
  • ESANN 1997 - Aspects of psychological computation in scene and face recognition [Details]
Luca Scionis
  • ESANN 2023 - Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization [Details]
T. Scutt
  • ESANN 1997 - Real neurons in real networks [Details]
J. Seal
  • ESANN 1995 - A distribution-based model of the dynamics of neural networks in the cerebral cortex [Details]
M. Sebag
  • ESANN 1999 - From first order logic to Nd: a data driven reformulation [Details]
P. Sebire
  • ESANN 1993 - MLP modular networks for multi-class recognition [Details]
Konstantinos Sechidis
  • ESANN 2017 - Algorithmic challenges in big data analytics [Details]
  • ESANN 2019 - Multi-target feature selection through output space clustering [Details]
Ismaila Seck
  • ESANN 2019 - L1-norm double backpropagation adversarial defense [Details]
Reza Sedghi
  • ESANN 2025 - Early Prediction of Dynamic Sparsity in Large Language Models [Details]
Michael Sedlmair
  • ESANN 2016 - Human-centered machine learning through interactive visualization: review and open challenges [Details]
  • ESANN 2018 - VisCoDeR: A tool for visually comparing dimensionality reduction algorithms [Details]
Matthias Seeger
  • ESANN 2008 - Learning Inverse Dynamics: a Comparison [Details]
Oliver Sefrin
  • ESANN 2025 - Encoding hyperspectral data with low-bond dimension quantum tensor networks [Details]
Itai Segall
  • ESANN 2017 - Deep convolutional neural networks for detecting noisy neighbours in cloud infrastructure [Details]
Enrique Carlos Segura
  • No papers found
S. Sehad
  • ESANN 1994 - Reinforcement learning and neural reinforcement learning [Details]
F. Seifart
  • ESANN 2003 - A neural model for heading detection from optic flow [Details]
Michael Seifert
  • ESANN 2012 - Posterior regularization and attribute assessment of under-determined linear mappings [Details]
Christin Seifert
  • ESANN 2026 - Weakly Supervised Shortcut Learning Mitigation Using Sparse Autoencoders [Details]
Udo Seiffert
  • 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]
Udo Seiffert
  • ESANN 2021 - Investigating Intensity and Transversal Drift in Hyperspectral Imaging Data [Details]
Udo Seiffert
  • No papers found
Udo Seiffert
  • ESANN 2004 - Theory and applications of neural maps [Details]
  • ESANN 2005 - Generalized Relevance LVQ with Correlation Measures for Biological Data [Details]
  • ESANN 2006 - Fuzzy image segmentation with Fuzzy Labelled Neural Gas [Details]
  • ESANN 2006 - Neural networks and machine learning in bioinformatics - theory and applications [Details]
  • ESANN 2006 - Sanger-driven MDSLocalize - a comparative study for genomic data [Details]
  • ESANN 2007 - Visualization of Fuzzy Information in Fuzzy-Classification for Image Segmentation using MDS [Details]
  • ESANN 2008 - Machine learning approches and pattern recognition for spectral data [Details]
  • ESANN 2010 - Validation of unsupervised clustering methods for leaf phenotype screening [Details]
  • ESANN 2011 - Recent trends in computational intelligence in life sciences [Details]
  • ESANN 2012 - Classifying Scotch Whisky from near-infrared Raman spectra with a Radial Basis Function Network with Relevance Learning [Details]
  • ESANN 2012 - Hardware accelerated real time classification of hyperspectral imaging data for coffee sorting [Details]
  • ESANN 2013 - Processing Hyperspectral Data in Machine Learning [Details]
  • ESANN 2019 - Transfer Learning for transferring machine-learning based models among hyperspectral sensors [Details]
Udo Seiffert
  • ESANN 2025 - Machine Learning on Smartphone-Captured Diffraction Data [Details]
U. Seiffert
  • ESANN 2001 - Multiple Layer Perceptron training using genetic algorithms [Details]
  • ESANN 2002 - Artificial Neural Networks on Massively Parallel Computer Hardware [Details]
  • ESANN 2003 - Digital Image Processing with Neural Networks [Details]
Borja Seijo-Pardo
  • ESANN 2016 - Using a feature selection ensemble on DNA microarray datasets [Details]
  • ESANN 2018 - Analysis of imputation bias for feature selection with missing data [Details]