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
Aldo Franco Dragoni
  • ESANN 2010 - Modeling contextualized textual knowledge as a Long-Term Working Memory [Details]
Pier Luigi Dragotti
  • ESANN 2017 - Solving Inverse Source Problems for Sources with Arbitrary Shapes using Sensor Networks [Details]
Noémie Draguet
  • ESANN 2025 - Making Convolutional Neural Networks Energy-Efficient: An Introduction [Details]
J.A. Drakopoulos
  • ESANN 1995 - Multi-sigmoidal units and neural networks [Details]
Matthew Draper
  • ESANN 2005 - performance of EMI based mine detection using back-propagation neural networks [Details]
J.P. Draye
  • ESANN 1993 - An efficient learning model for the neural integrator of the oculomotor system [Details]
  • ESANN 1997 - Evidence of efficiency of recurrent neural networks with ARMA-like units [Details]
J.-P. Draye
  • ESANN 1995 - Active noise control with dynamic recurrent neural networks [Details]
  • ESANN 1995 - Identification of the human arm kinetics using dynamic recurrent neural networks [Details]
  • ESANN 1996 - Adaptative time constants improve the dynamic features of recurrent neural networks [Details]
  • ESANN 1996 - Negative initial weights improve learning in recurrent neural networks [Details]
  • ESANN 1998 - Control of a subsonic electropneumatic acoustic generator with dynamic recurrent neural networks [Details]
Philippe Dreesen
  • 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]
E. Drege
  • ESANN 1998 - Face recognition: pre-processing techniques for linear autoassociators [Details]
P. Drezet
  • ESANN 1999 - An efficient formulation of sparsity controlled support vector regression [Details]
Fabrice Druaux
  • ESANN 2009 - Self-organising map for large scale processes monitoring [Details]
F. Druaux
  • ESANN 2003 - Autonomous learning algorithm for fully connected recurrent networks [Details]
Madalina Drugan
  • ESANN 2014 - Linear Scalarized Knowledge Gradient in the Multi-Objective Multi-Armed Bandits Problem [Details]
  • ESANN 2015 - Bernoulli bandits: an empirical comparison [Details]
  • ESANN 2015 - Multi-objective optimization perspectives on reinforcement learning algorithms using reward vectors [Details]
Georges Adrian Drumea
  • ESANN 2007 - Tracking fast changing non-stationary distributions with a topologically adaptive neural network: application to video tracking [Details]
Linda Drupsteen
  • ESANN 2015 - Enhancing learning at work. How to combine theoretical and data-driven approaches, and multiple levels of data? [Details]
Manxing Du
  • ESANN 2017 - The Top 10 Topics in Machine Learning Revisited: A Quantitative Meta-Study [Details]
  • ESANN 2019 - time series modelling of market price in real-time bidding [Details]
Julio Cesar Duarte
  • ESANN 2009 - Improving BAS committee performance with a semi-supervised approach [Details]
Davi Duarte de Paula
  • ESANN 2024 - Generation of Simulated Dataset of Computed Tomography Images of Eggs and Extraction of Measurements Using Deep Learning [Details]
Mathieu Dubois
  • ESANN 2011 - Visual place recognition using Bayesian filtering with Markov chains [Details]
O. Dubois
  • ESANN 1999 - On the invertibility of the RBF model in a predictive control strategy [Details]
W. Duch
  • ESANN 1996 - On global self-organizing maps [Details]
  • ESANN 1997 - Extraction of crisp logical rules using constrained backpropagation networks [Details]
  • ESANN 2001 - Constructive density estimation network based on several different separable transfer functions [Details]
  • ESANN 2001 - Optimal transfer function neural networks [Details]
  • ESANN 2001 - Transfer functions: hidden possibilities for better neural networks [Details]
Laurine Duchesne
  • ESANN 2021 - Supervised learning of convex piecewise linear approximations of optimization problems [Details]
Daniel Duclos
  • ESANN 2016 - anomaly detection on spectrograms using data-driven and fixed dictionary representations [Details]
Melanie Ducoffe
  • ESANN 2017 - Active learning strategy for CNN combining batchwise Dropout and Query-By-Committee [Details]
Charlotte Ducuing
  • ESANN 2019 - Fairness and Accountability of Machine Learning Models in Railway Market: are Applicable Railway Laws Up to Regulate Them? [Details]
Pete Duell
  • ESANN 2006 - Diversity creation in local search for the evolution of neural network ensembles [Details]
Siegmund Duell
  • ESANN 2010 - The Markov Decision Process Extraction Network [Details]
  • ESANN 2012 - Recurrent Neural State Estimation in Domains with Long-Term Dependencies [Details]
  • ESANN 2013 - Ensembles for Continuous Actions in Reinforcement Learning [Details]
R. Duin
  • ESANN 1999 - Data domain description using support vectors [Details]
Haris Dukic
  • ESANN 2021 - Inductive learning for product assortment graph completion [Details]
Siegmund Düll
  • ESANN 2014 - Exploiting similarity in system identification tasks with recurrent neural networks [Details]