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
Tayfun Alpay
  • ESANN 2018 - An analysis of subtask-dependency in robot command interpretation with dilated CNNs [Details]
  • ESANN 2018 - Surprisal-based activation in recurrent neural networks [Details]
René Alquézar Mancho
  • ESANN 2014 - Misclassification of class C G-protein-coupled receptors as a label noise problem [Details]
Pierre Alquier
  • ESANN 2017 - Non-negative matrix factorization as a pre-processing tool for travelers temporal profiles clustering [Details]
Nawal Alqurashi
  • ESANN 2024 - Decision fusion based multimodal hierarchical method for speech emotion recognition from audio and text [Details]
Stephen Alstrup
  • ESANN 2015 - High-School Dropout Prediction Using Machine Learning: A Danish Large-scale Study [Details]
  • ESANN 2019 - Detecting Ghostwriters in High Schools [Details]
Tomas Aluja
  • ESANN 2013 - Handling missing values in kernel methods with application to microbiology data [Details]
Juan Carlos Alvarado-Pérez
  • ESANN 2015 - Geometrical homotopy for data visualization [Details]
Diego Álvarez
  • ESANN 2024 - Human Activity Recognition from Thigh and Wrist Accelerometry [Details]
M.R. Alvarez
  • ESANN 1998 - Separation of sources in a class of post-nonlinear mixtures [Details]
  • ESANN 2001 - A stochastic and competitive network for the separation of sources [Details]
Juan Carlos Alvarez
  • ESANN 2024 - Human Activity Recognition from Thigh and Wrist Accelerometry [Details]
M. Alvarez
  • ESANN 1994 - Decoding functions for Kohonen maps [Details]
Stephanie Alvarez Fernandez
  • ESANN 2016 - An Immune-Inspired, Dependence-Based Approach to Blind Inversion of Wiener Systems [Details]
Diego Álvarez-Estévez
  • ESANN 2016 - Automatic detection of EEG arousals [Details]
  • ESANN 2017 - Outlining a simple and robust method for the automatic detection of EEG arousals [Details]
Andres Marino Alvarez-Meza
  • ESANN 2013 - Automatic Singular Spectrum Analysis for Time-Series Decomposition [Details]
Ricardo Alvear-Sandoval
  • ESANN 2016 - An Experiment in Pre-Emphasizing Diversified Deep Neural Classifiers [Details]
Alexessander Alves
  • ESANN 2006 - Selection of more than one gene at a time for cancer prediction from gene expression data [Details]
Marcos Antonio Alves
  • ESANN 2018 - An extension of nonstationary fuzzy sets to heteroskedastic fuzzy time series [Details]
Felippe Alves
  • ESANN 2019 - Trust, law and ideology in a NN agent model of the US Appellate Courts [Details]
Daniel Alves
  • ESANN 2014 - Credit analysis with a clustering RAM-based neural classifier [Details]
Tiago Alves
  • ESANN 2020 - Towards Adversarial Attack Resistant Deep Neural Networks [Details]
Juliana Assis Alves
  • ESANN 2025 - Enhancing Image Classification in Quantum Computing: A Study on Preprocessing Techniques and Qubit Limitations [Details]
Carlos Alzate
  • ESANN 2010 - Highly sparse kernel spectral clustering with predictive out-of-sample extensions [Details]
  • ESANN 2011 - Symbolic computing of LS-SVM based models [Details]
  • ESANN 2014 - Optimal Data Projection for Kernel Spectral Clustering [Details]
M. Amamiya
  • ESANN 2001 - SOM competition for complex image scene with variant object positions [Details]
Hana Amar
  • No papers found
Victor Amaral De Sousa
  • ESANN 2018 - Structuring and Solving Multi-Criteria Decision Making Problems using Artificial Neural Networks: a smartphone recommendation case [Details]
S. Amari
  • ESANN 2003 - On different ensembles of kernel machines [Details]
Shun-ichi Amari
  • ESANN 2016 - Maximum likelihood learning of RBMs with Gaussian visible units on the Stiefel manifold [Details]
S.-I. Amari
  • ESANN 1997 - Nonlinearity and separation capability: further justification for the ICA algorithm with a learned mixture of parametric densities [Details]
Umberto Amato
  • ESANN 2012 - Range-based non-orthogonal ICA using cross-entropy method [Details]
Leonardo Amato
  • ESANN 2023 - Real-time Detection of Evoked Potentials by Deep Learning: a Case Study [Details]