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
X. Parra
  • ESANN 2001 - Learning fault-tolerance in Radial Basis Function Networks [Details]
  • ESANN 2002 - An unified framework for 'All data at once' multi-class Support Vector Machines [Details]
Samuel Parsons
  • ESANN 2018 - Sleep staging with deep learning: a convolutional model [Details]
aaron parsons
  • ESANN 2020 - Navigational Freespace Detection for Autonomous Driving in Fixed Routes [Details]
Johannes Partzsch
  • ESANN 2009 - On the routing complexity of neural network models - Rent's Rule revisited [Details]
  • ESANN 2010 - A critique of BCM behavior verification for STDP-type plasticity models [Details]
Eli Parviainen
  • ESANN 2011 - Effects of sparseness and randomness of pairwise distance matrix on t-SNE results [Details]
Elina Parviainen
  • ESANN 2010 - Reliability of dimension reduction visualizations of hierarchical structures [Details]
Luca Pasa
  • ESANN 2020 - Deep Recurrent Graph Neural Networks [Details]
  • ESANN 2020 - Linear Graph Convolutional Networks [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]
Luca Pasa
  • ESANN 2014 - A HMM-based pre-training approach for sequential data [Details]
Luca Pasa
  • ESANN 2024 - Informed Machine Learning for Complex Data [Details]
  • ESANN 2024 - Towards the application of Backpropagation-Free Graph Convolutional Networks on Huge Datasets [Details]
  • ESANN 2025 - Foundation and Generative Models for Graphs [Details]
F. Pasemann
  • ESANN 1999 - Synchronizing chaotic neuromodules [Details]
  • ESANN 2003 - Evolved Neurodynamics for Robot Control [Details]
E. Pasero
  • ESANN 1994 - Combining multi-layer perceptrons in classification problems [Details]
Kevin Pasini
  • ESANN 2024 - Evaluation methodology for disentangled uncertainty quantification on regression models [Details]
A. Pasley
  • ESANN 2002 - High frequency forecasting with associative memories [Details]
Piter Pasma
  • ESANN 2006 - Classification of Boar Sperm Head Images using Learning Vector Quantization [Details]
Jean-Baptiste Passot
  • ESANN 2009 - Cerebellum and spatial cognition: A connectionist approach [Details]
Sunya Pasuk
  • ESANN 2011 - Stability of Neural Network Control for Uncertain Sampled-Data Systems [Details]
R. Patacchi
  • ESANN 2001 - Applications of neuro-fuzzy classification, evaluation and forecasting techniques in agriculture [Details]
M. Patel
  • ESANN 1996 - Investigating lexical access using neural nets [Details]
Leena Patel
  • ESANN 2006 - Evolving multi-segment 'super-lamprey' CPG's for increased swimming control [Details]
Sukanya Patra
  • ESANN 2023 - Anomaly detection in irregular image sequences for concentrated solar power plants [Details]
Benoit Patra
  • ESANN 2012 - A Discussion on Parallelization Schemes for Stochastic Vector Quantization Algorithms [Details]
Fleury Patrice
  • ESANN 2004 - Neural Hardware: beyond ones and zeros [Details]
R. J. Patton
  • ESANN 2002 - Neural networks for fault diagnosis of industrial plants at different working points [Details]
R.J. Patton
  • ESANN 2002 - Fault diagnosis of an electro-pneumatic valve actuator using neural networks with fuzzy capabilities [Details]
Florian Patzelt
  • ESANN 2019 - Conditional WGAN for grasp generation [Details]
Stefan Patzke
  • ESANN 2025 - Generating Synthetic Spectral Data using Conditional DDPM [Details]
H. Paugam-Moisy
  • ESANN 2002 - Neural networks for modeling memory : case studies [Details]
Hélène Paugam-Moisy
  • ESANN 2006 - Cluster detection algorithm in neural networks [Details]
  • ESANN 2006 - Learning and discrimination through STDP in a top-down modulated associative memory [Details]
  • ESANN 2006 - Saliency extraction with a distributed spiking neural network [Details]
  • ESANN 2007 - A supervised learning approach based on STDP and polychronization in spiking neuron networks [Details]
  • ESANN 2008 - Neural networks for computational neuroscience [Details]
  • ESANN 2011 - An Introduction to Deep Learning [Details]
  • ESANN 2012 - From neuronal cost-based metrics towards sparse coded signals classification [Details]
Jérôme Paul
  • ESANN 2012 - The stability of feature selection and class prediction from ensemble tree classifiers [Details]
  • ESANN 2014 - Kernel methods for mixed feature selection [Details]
Jérôme Paul
  • ESANN 2024 - Transfer learning to minimize the predictive risk in clinical research [Details]