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
I. Harvey
  • ESANN 1993 - Incremental evolution of neural network architectures for adaptive behavior [Details]
Robert Haschke
  • ESANN 2004 - Input Space Bifurcation Manifolds of RNNs [Details]
  • ESANN 2010 - Finding correlations in multimodal data using decomposition approaches [Details]
  • ESANN 2013 - Perceptual grouping through competition in coupled oscillator networks [Details]
  • ESANN 2019 - Conditional WGAN for grasp generation [Details]
Alexander Hasenfuss
  • ESANN 2006 - Magnification control for batch neural gas [Details]
  • ESANN 2008 - Magnification Control in Relational Neural Gas [Details]
Atsushi Hashimoto
  • ESANN 2011 - Abstract category learning [Details]
M. Hasler
  • ESANN 1995 - Self-organisation, metastable states and the ODE method in the Kohonen neural network [Details]
Werner Hauptmann
  • ESANN 2008 - Interpretable ensembles of local models for safety-related applications [Details]
  • ESANN 2009 - Heterogeneous mixture-of-experts for fusion of locally valid knowledge-based submodels [Details]
Cécile Hautecoeur
  • ESANN 2020 - Image completion via nonnegative matrix factorization using B-splines [Details]
Cécile Hautecoeur
  • ESANN 2019 - Nonnegative matrix factorization with polynomial signals via hierarchical alternating least squares [Details]
Mohammad Hawarat
  • ESANN 2005 - The Nonlinear Dynamic State neuron [Details]
Peter Haycock
  • ESANN 2010 - Extending reservoir computing with random static projections: a hybrid between extreme learning and RC [Details]
G. Hayes
  • ESANN 1998 - A self-organising neural network for modelling cortical development [Details]
  • ESANN 1998 - Learning sensory-motor cortical mappings without training [Details]
J. Hayes
  • ESANN 2002 - Why will rat's go where rats will not? [Details]
S. Haykin
  • ESANN 1999 - Generalized support vector machines [Details]
M. Haylock
  • ESANN 2003 - Statistical downscaling with artificial neural networks [Details]
R. Hayward
  • ESANN 2003 - Extracting Interface Assertions from Neural Networks in Polyhedral Format [Details]
Aurélien Hazan
  • ESANN 2013 - Frequency-Dependent Peak-Over-Threshold algorithm for fault detection in the spectral domain [Details]
Zhaoshui He
  • No papers found
Haibo He
  • ESANN 2014 - Learning and modeling big data [Details]
FAN HE
  • ESANN 2020 - A Real-time PCB Defect Detector Based on Supervised and Semi-supervised Learning [Details]
Ramya Hebbalaguppe
  • ESANN 2020 - An Empirical Study of Iterative Knowledge Distillation for Neural Network Compression [Details]
Georges Hébrail
  • ESANN 2009 - Simultaneous Clustering and Segmentation for Functional Data [Details]
Thomas Hecht
  • ESANN 2015 - Resource-efficient Incremental learning in very high dimensions [Details]
  • ESANN 2015 - Using self-organizing maps for regression: the importance of the output function [Details]
  • ESANN 2016 - Towards incremental deep learning: multi-level change detection in a hierarchical visual recognition architecture [Details]
Martin Heckmann
  • ESANN 2007 - A hierarchical model for syllable recognition [Details]
Michael A. Hedderich
  • ESANN 2021 - Estimating Formulas for Model Performance Under Noisy Labels Using Symbolic Regression [Details]
Kai Heesche
  • ESANN 2009 - Heterogeneous mixture-of-experts for fusion of locally valid knowledge-based submodels [Details]
Srinidhi Hegde
  • ESANN 2020 - An Empirical Study of Iterative Knowledge Distillation for Neural Network Compression [Details]
István Hegedűs
  • ESANN 2014 - Lightning fast asynchronous distributed k-means clustering [Details]
István Hegedűs
  • ESANN 2020 - Attacking Model Sets with Adversarial Examples [Details]
Istvan Hegedus
  • ESANN 2019 - Adversarial robustness of linear models: regularization and dimensionality [Details]
G. Heidemann
  • ESANN 2002 - Combining gestural and contact information for visual guidance of multi-finger grasps [Details]
  • ESANN 2003 - Semi-automatic acquisition and labelling of image data using SOMs [Details]