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
Pablo Barros
  • ESANN 2020 - CNN Encoder to Reduce the Dimensionality of Data Image for Motion Planning [Details]
Filipa S. Barros
  • ESANN 2024 - Leveraging Physics-Informed Neural Networks as Solar Wind Forecasting Models [Details]
De Moor Bart
  • ESANN 2007 - Convex optimization for the design of learning machines [Details]
Marius Bartcus
  • ESANN 2014 - Bayesian non-parametric parsimonious clustering [Details]
Erhardt Barth
  • ESANN 2008 - Learning Data Representations with Sparse Coding Neural Gas [Details]
Erhardt Barth
  • ESANN 2024 - AI-based Collimation Optimization for X-Ray Imaging using Time-of-Flight Cameras [Details]
  • ESANN 2025 - Investigating the Impact of Imbalanced Medical Data on the Performance of Self-Supervised Learning Approaches [Details]
Quentin Barthélemy
  • ESANN 2012 - From neuronal cost-based metrics towards sparse coded signals classification [Details]
Alan Barton
  • No papers found
Debjanee Barua
  • No papers found
Marek Barwiński
  • ESANN 2006 - A sequence-encoding neural network for face recognition [Details]
Patrick Bas
  • ESANN 2008 - A Methodology for Building Regression Models using Extreme Learning Machine: OP-ELM [Details]
  • ESANN 2010 - Ensemble Modeling with a Constrained Linear System of Leave-One-Out Outputs [Details]
Erhan Bas
  • ESANN 2010 - Principal curve tracing [Details]
Nicolas Baskiotis
  • ESANN 2016 - Learning Embeddings for Completion and Prediction of Relationnal Multivariate Time-Series [Details]
  • ESANN 2017 - Anomaly detection and characterization in smart card logs using NMF and Tweets [Details]
Ariel Basso Madjoukeng
  • ESANN 2025 - Benchmarking Data Augmentation for Contrastive Learning in Static Sign Language Recognition [Details]
Sebastian Basterrech
  • ESANN 2024 - Exploring Self-Organizing Maps for Addressing Semantic Impairments [Details]
Sebastián Basterrech
  • ESANN 2018 - Estimation of the Human Concentration using Echo State Networks [Details]
  • ESANN 2018 - Quantifying the Reservoir Quality using Dimensionality Reduction Techniques [Details]
Saikat Basu
  • ESANN 2015 - Learning Sparse Feature Representations using Probabilistic Quadtrees and Deep Belief Nets [Details]
John Batchelor
  • ESANN 2012 - Highly efficient localisation utilising weightless neural systems [Details]
Kim Batselier
  • ESANN 2012 - Joint Regression and Linear Combination of Time Series for Optimal Prediction [Details]
  • ESANN 2012 - Weighted/Structured Total Least Squares problems and polynomial system solving [Details]
  • ESANN 2012 - maximum likelihood estimation and polynomial system solving [Details]
Sebastiano Battiato
  • ESANN 2024 - Federated Learning in a Semi-Supervised Environment for Earth Observation Data [Details]
M. Battipede
  • ESANN 2000 - A comparative design of a MIMO neural adaptive rate damping for a nonlinear helicopter model [Details]
  • ESANN 2003 - Post-failure analysis of an adaptive predictor-corrector neural controller on a flight simulator [Details]
Christian Bauckhage
  • ESANN 2021 - Toxicity Detection in Online Comments with Limited Data: A Comparative Analysis [Details]
  • No papers found
H.-U. Bauer
  • ESANN 1997 - Measuring topology preservation in maps of real-world data [Details]
Dietmar Bauer
  • ESANN 2010 - Fast and good initialization of RBF networks [Details]
Jill Baumann
  • ESANN 2023 - Wind Power Prediction with ETSformer [Details]
  • ESANN 2024 - Towards Explainable Evolution Strategies with Large Language Models [Details]
  • ESANN 2025 - Unlocking Structured Thinking in Language Models with Cognitive Prompting [Details]
Tristan Baumann
  • ESANN 2021 - Hierarchical Planning in Multilayered State-Action Networks [Details]
Alfred Bauny
  • ESANN 2024 - A Kalman Filter and Neural Network Hybrid Approach for Health Monitoring of Aircraft Engines [Details]
Bruno Bauwens
  • ESANN 2010 - Directional predictions for 4-class BCI data [Details]
Delphine Bay
  • ESANN 2021 - Convolutional Neural Network Architecture for Classification of Aircraft Engines Flight Time Series [Details]