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
Mario Valerio Giuffrida
  • ESANN 2024 - Federated Learning in a Semi-Supervised Environment for Earth Observation Data [Details]
D. Giuliani
  • ESANN 1997 - Speaker normalization with a mixture of recurrent networks [Details]
Claudio Giuliano
  • ESANN 2009 - A semi-supervised approach to question classification [Details]
Tobias Glasmachers
  • ESANN 2006 - Degeneracy in model selection for SVMs with radial Gaussian kernel [Details]
  • ESANN 2008 - On related violating pairs for working set selection in SMO algorithms [Details]
Patrick Glauner
  • ESANN 2017 - The Top 10 Topics in Machine Learning Revisited: A Quantitative Meta-Study [Details]
  • ESANN 2018 - Impact of Biases in Big Data [Details]
François Glineur
  • ESANN 2013 - A nuclear-norm based convex formulation for informed source separation [Details]
  • ESANN 2013 - ONP-MF: An Orthogonal Nonnegative Matrix Factorization Algorithm with Application to Clustering [Details]
  • ESANN 2019 - Nonnegative matrix factorization with polynomial signals via hierarchical alternating least squares [Details]
François Glineur
  • ESANN 2020 - Image completion via nonnegative matrix factorization using B-splines [Details]
  • ESANN 2020 - Lower bounds on the nonnegative rank using a nested polytopes formulation [Details]
  • ESANN 2021 - Transfer learning in Bayesian optimization for the calibration of a beam line in proton therapy [Details]
  • ESANN 2024 - Convergence analysis of an inexact gradient method on smooth convex functions [Details]
Pierre Glize
  • ESANN 2005 - Organization properties of open networks of cooperative neuro-agents [Details]
Hervé Glotin
  • ESANN 2012 - Functional Mixture Discriminant Analysis with hidden process regression for curve classification [Details]
  • ESANN 2014 - Bayesian non-parametric parsimonious clustering [Details]
Dorota Glowacka
  • ESANN 2013 - Content-based image retrieval with hierarchical Gaussian Process bandits with self-organizing maps [Details]
Stefan Glüge
  • ESANN 2013 - Auto-encoder pre-training of segmented-memory recurrent neural networks [Details]
Giorgio Gnecco
  • ESANN 2016 - Learning with hard constraints as a limit case of learning with soft constraints [Details]
E. Godaux
  • ESANN 1993 - An efficient learning model for the neural integrator of the oculomotor system [Details]
Lalit Goel
  • ESANN 2014 - Electric load forecasting using wavelet transform and extreme learning machine [Details]
Kratarth Goel
  • No papers found
Lucas Goené
  • ESANN 2024 - Dual Stream Graph Transformer Fusion Networks for Enhanced Brain Decoding [Details]
C. Goerick
  • ESANN 1996 - On unlearnable problems -or- A model for premature saturation in backpropagation learning [Details]
Christian Goerick
  • ESANN 2007 - A hierarchical model for syllable recognition [Details]
  • ESANN 2008 - Computationally Efficient Neural Field Dynamics [Details]
Etienne Goffinet
  • ESANN 2021 - Multivariate Time Series Multi-Coclustering. Application to Advanced Driving Assistance System Validation [Details]
Wee Jin Goh
  • ESANN 2005 - The Nonlinear Dynamic State neuron [Details]
  • ESANN 2007 - Human motion recognition using Nonlinear Transient Computation [Details]
  • ESANN 2007 - Pattern Recognition using Chaotic Transients [Details]
Slawomir Golak
  • ESANN 2021 - Correlated Weights Neural Layer with external control [Details]
Jean Golay
  • ESANN 2015 - Morisita-based feature selection for regression problems [Details]
Boris Golden
  • ESANN 2015 - Reducing offline evaluation bias of collaborative filtering [Details]
  • ESANN 2015 - Using the Mean Absolute Percentage Error for Regression Models [Details]
C. Goller
  • ESANN 1999 - Learning search-control heuristics for automated deduction systems with folding architecture networks [Details]
Joao Gomes
  • 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]
  • ESANN 2019 - Sparse minimal learning machine using a diversity measure minimization [Details]
João Gomes
  • No papers found
João Gomes
  • ESANN 2022 - Predicting Test Execution Times with Asymmetric Random Forests [Details]
Heitor Murilo Gomes
  • ESANN 2018 - Adaptive random forests for data stream regression [Details]
Juan Gómez
  • ESANN 2011 - Growing Hierarchical Sectors on Sectors [Details]
Faustino Gomez
  • ESANN 2006 - Evolino for recurrent support vector machines [Details]