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
Giuseppe Floris
  • ESANN 2023 - Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization [Details]
A.J.B. Fogg
  • ESANN 2002 - A data vizualisation method for investigating the reliability of a high-dimensional low-back-pain MLP network [Details]
Pierfrancesco Foglia
  • ESANN 2025 - Explainable ensemble learning for structural damage prediction under seismic events [Details]
Samuele Fonio
  • ESANN 2023 - Hierarchical priors for Hyperspherical Prototypical Networks [Details]
  • ESANN 2024 - FedHP: Federated Learning with Hyperspherical Prototypical Regularization [Details]
  • ESANN 2026 - When Curvature Counts: Hyperbolic Geometry in Prototype-Based Image Classification [Details]
Jose Fonseca
  • ESANN 2013 - Are Rosenblatt multilayer perceptrons more powerfull than sigmoidal multilayer perceptrons? From a counter example to a general result [Details]
  • ESANN 2014 - A New Error-Correcting Syndrome Decoder with Retransmit Signal Implemented with an Hardlimit Neural Network [Details]
Gabriel Fonseca
  • ESANN 2026 - Hierarchical Multi-Scale Deep Neural Network for Schizophrenia Detection in Neuroimaging [Details]
Raul Fonseca Neto
  • ESANN 2019 - A best-first branch-and-bound search for solving the transductive inference problem using support vector machines [Details]
Patrick Fontaine
  • ESANN 2022 - Feature Compression Using Dynamic Switches in Multi-split CNNs [Details]
Patrick Fontaine
  • No papers found
Bertrand Fontaine
  • ESANN 2007 - Bat echolocation modelling using spike kernels with Support Vector Regression. [Details]
Michele Fontanesi
  • ESANN 2024 - XAI and Bias of Deep Graph Networks [Details]
Julie Fontecave-Jallon
  • ESANN 2021 - Fusion of estimations from two modalities using the Viterbi's algorithm: application to fetal heart rate monitoring [Details]
O. Fontenla-Romero
  • ESANN 2003 - Accelerating the convergence speed of neural networks learning methods using least squares [Details]
  • ESANN 2003 - Recursive Least Squares for an Entropy Regularized MSE Cost Function [Details]
  • ESANN 2003 - Self-organizing maps and functional networks for local dynamic modeling [Details]
Oscar Fontenla-Romero
  • ESANN 2004 - Shear strength prediction using dimensional analysis and functional networks [Details]
  • ESANN 2008 - A Method for Time Series Prediction using a Combination of Linear Models [Details]
  • ESANN 2008 - A Regularized Learning Method for Neural Networks Based on Sensitivity Analysis [Details]
  • ESANN 2011 - A distributed learning algorithm based on two-layer artificial neural networks and genetic algorithms [Details]
  • ESANN 2012 - One-class classifier based on extreme value statistics [Details]
  • ESANN 2014 - Modeling consumption of contents and advertising in online newspapers [Details]
  • ESANN 2016 - A fast learning algorithm for high dimensional problems: an application to microarrays [Details]
  • ESANN 2016 - Distributed learning algorithm for feedforward neural networks [Details]
  • ESANN 2016 - One-class classification algorithm based on convex hull [Details]
  • ESANN 2017 - Mutual information for improving the efficiency of the SCH algorithm [Details]
  • ESANN 2018 - LANN-DSVD: A privacy-preserving distributed algorithm for machine learning [Details]
Oscar Fontenla-Romero
  • ESANN 2021 - Federated Learning approach for SpectralClustering [Details]
  • ESANN 2026 - FedHENet: A Frugal Federated Learning Framework for Heterogeneous Environments [Details]
M. Forcada
  • ESANN 1999 - Neural learning of approximate simple regular languages [Details]
M.L. Forcada
  • ESANN 1999 - Encoding of sequential translators in discrete-time recurrent neural nets [Details]
Florent Forest
  • ESANN 2024 - From Data to Simulation: Capturing Aircraft Engine Degradation Dynamics [Details]
Florent Forest
  • ESANN 2019 - Deep Embedded SOM: joint representation learning and self-organization [Details]
Loris Foresti
  • ESANN 2010 - Time series input selection using multiple kernel learning [Details]
Håkan Forsberg
  • ESANN 2026 - Physics-Informed Recurrent Architecture with Embedded Thermodynamic Dynamics for Robust Sequence Modeling [Details]
Nils Förster
  • ESANN 2026 - Revisiting Neural Activation Coverage for Uncertainty Estimation [Details]
Alexander Förster
  • ESANN 2007 - RNN-based Learning of Compact Maps for Efficient Robot Localization [Details]
J.C. Fort
  • ESANN 1994 - A lateral contribution learning algorithm for multi MLP architecture [Details]
  • ESANN 1994 - Two or three things that we know about the Kohonen algorithm [Details]
J.-C. Fort
  • ESANN 1994 - A non linear Kohonen algorithm [Details]
  • ESANN 1995 - About the Kohonen algorithm: strong or weak self-organization? [Details]
  • ESANN 1996 - Quantization vs Organization in the Kohonen S.O.M. [Details]
  • ESANN 1997 - Almost sure convergence of the one-dimensional Kohonen algorithm [Details]
  • ESANN 2002 - Advantages and drawbacks of the Batch Kohonen algorithm [Details]
Luigi Fortuna
  • ESANN 2008 - An automatic identifier of Confinement Regimes at JET combining Fuzzy Logic and Classification Trees [Details]
L. Fortuna
  • ESANN 1998 - Cellular neural networks: from chaos generation to compexity modelling [Details]
Adrien Foucart
  • ESANN 2025 - Ranking the scores of algorithms with confidence [Details]
François Fouss
  • ESANN 2005 - clustering using a random walk based distance measure [Details]
  • ESANN 2014 - Improving accuracy by reducing the importance of hubs in nearest-neighbor recommendations [Details]
  • ESANN 2014 - The Sum-over-Forests clustering [Details]
R.J. Foxall
  • ESANN 2002 - Heteroscedastic regularised kernel regression for prediction of episodes of poor air quality [Details]
  • ESANN 2003 - Approximately unbiased estimation of conditional variance in heteroscedastic kernel ridge regression [Details]