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
Georgios Naros
  • ESANN 2013 - Decoding stimulation intensity from evoked ECoG activity using support vector regression [Details]
Daniel Nascimento
  • ESANN 2015 - A WiSARD-based multi-term memory framework for online tracking of objects [Details]
Fawzi Nashashibi
  • ESANN 2017 - Fusion of Stereo Vision for Pedestrian Recognition using Convolutional Neural Networks [Details]
  • ESANN 2019 - Improving Pedestrian Recognition using Incremental Cross Modality Deep Learning [Details]
Samaneh Nasiri Ghosheh Bolagh
  • ESANN 2016 - Unsupervised Cross-Subject BCI Learning and Classification using Riemannian Geometry [Details]
René Natowicz
  • ESANN 2008 - A new method of DNA probes selection and its use with multi-objective neural network for predicting the outcome of breast cancer preoperative chemotherapy [Details]
  • ESANN 2013 - GA-KDE-Bayes: an evolutionary wrapper method based on non-parametric density estimation applied to bioinformatics problems [Details]
T. Natschläger
  • ESANN 1999 - Fast analog computation in networks of spiking neurons using unreliable synapses [Details]
T. Nattkemper
  • ESANN 2000 - A neural network architecture for automatic segmentation of fluorescence micrographs [Details]
A. Naud
  • ESANN 1996 - On global self-organizing maps [Details]
G. Naulaers
  • ESANN 2002 - Prediction of mental development of preterm newborns at birth time using LS-SVM [Details]
Valeriya Naumova
  • No papers found
C. Nautze
  • ESANN 1997 - Extended Bayesian learning [Details]
Nicolò Navarin
  • ESANN 2015 - Exploiting the ODD framework to define a novel effective graph kernel [Details]
  • ESANN 2016 - Advances in Learning with Kernels: Theory and Practice in a World of growing Constraints [Details]
  • ESANN 2016 - Measuring the Expressivity of Graph Kernels through the Rademacher Complexity [Details]
  • ESANN 2017 - Approximated Neighbours MinHash Graph Node Kernel [Details]
  • ESANN 2017 - Fast hyperparameter selection for graph kernels via subsampling and multiple kernel learning [Details]
  • ESANN 2018 - DEEP: decomposition feature enhancement procedure for graphs [Details]
  • ESANN 2018 - Emerging trends in machine learning: beyond conventional methods and data [Details]
  • ESANN 2019 - On the definition of complex structured feature spaces [Details]
Nicolò Navarin
  • ESANN 2020 - A Systematic Assessment of Deep Learning Models for Molecule Generation [Details]
  • ESANN 2020 - Deep Recurrent Graph Neural Networks [Details]
  • ESANN 2020 - Learning Deep Fair Graph Neural Networks [Details]
  • ESANN 2020 - Linear Graph Convolutional Networks [Details]
  • ESANN 2021 - Complex Data: Learning Trustworthily, Automatically, and with Guarantees [Details]
  • ESANN 2021 - Tangent Graph Convolutional Network [Details]
  • ESANN 2022 - Biased Edge Dropout in NIFTY for Fair Graph Representation Learning [Details]
  • ESANN 2022 - Deep Learning for Graphs [Details]
  • ESANN 2023 - An Empirical Study of Over-Parameterized Neural Models based on Graph Random Features [Details]
  • ESANN 2023 - Graph Representation Learning [Details]
  • ESANN 2024 - Informed Machine Learning for Complex Data [Details]
  • ESANN 2024 - Towards the application of Backpropagation-Free Graph Convolutional Networks on Huge Datasets [Details]
David Nebel
  • ESANN 2014 - Supervised Generative Models for Learning Dissimilarity Data [Details]
  • ESANN 2015 - Median-LVQ for classification of dissimilarity data based on ROC-optimization [Details]
  • ESANN 2016 - Adaptive dissimilarity weighting for prototype-based classification optimizing mixtures of dissimilarities [Details]
Angela Nebot
  • ESANN 2006 - Learning what is important: feature selection and rule extraction in a virtual course [Details]
  • ESANN 2007 - Identification of churn routes in the Brazilian telecommunications market [Details]
  • ESANN 2013 - Visualizing pay-per-view television customers churn using cartograms and flow maps [Details]
R.P. Neco
  • ESANN 1999 - Encoding of sequential translators in discrete-time recurrent neural nets [Details]
Benjamin Negrevergne
  • ESANN 2020 - Fréchet Mean Computation in Graph Space through Projected Block Gradient Descent [Details]
Paul Neilson
  • ESANN 2015 - Measuring scoring efficiency through goal expectancy estimation [Details]
E. Nelle
  • ESANN 1995 - Spatial summation in simple cells: computational and experimental results [Details]
John Nelson
  • ESANN 2013 - Sensor Positioning for Activity Recognition Using Multiple Accelerometer-Based Sensors [Details]
Ramakrishna Nemani
  • ESANN 2015 - Learning Sparse Feature Representations using Probabilistic Quadtrees and Deep Belief Nets [Details]
Predrag Neskovic
  • ESANN 2007 - Classifying n-back EEG data using entropy and mutual information features [Details]
  • ESANN 2007 - Feature extraction for EEG classification: representing electrode outputs as a Markov stochastic process [Details]
L. Neuberg
  • ESANN 1995 - XOR and backpropagation learning: in and out of the chaos? [Details]
Oren Neumann
  • No papers found
H. Neumann
  • ESANN 1996 - Neural model for visual contrast detection [Details]
  • ESANN 1999 - Recurrent V1-V2 interaction for early visual information processing [Details]
  • ESANN 2003 - A neural model for heading detection from optic flow [Details]
  • ESANN 2003 - A view-based approach for object recognition from image sequences [Details]
Oren Neumann
  • ESANN 2022 - Size Scaling in Self-Play Reinforcement Learning [Details]
Klaus Neumann
  • ESANN 2012 - intrinsic plasticity via natural gradient descent [Details]
  • ESANN 2013 - Neurally imprinted stable vector fields [Details]
R. Neuneier
  • ESANN 2002 - Undershooting: modeling dynamical systems by time grid refinements [Details]
J. Neves
  • ESANN 2001 - Lamarckian training of feedforward neural networks [Details]
  • ESANN 2003 - Adaptive Learning in Changing Environments [Details]
Aline Neves
  • ESANN 2013 - Error entropy criterion in echo state network training [Details]