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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
L. Pessoa
  • ESANN 1996 - Neural model for visual contrast detection [Details]
R. Petacchi
  • ESANN 2002 - Neuro-fuzzy methodologies for the clustering and the reliability estimation of olive fruit fly infestation [Details]
Diego Peteiro-Barral
  • ESANN 2011 - A distributed learning algorithm based on two-layer artificial neural networks and genetic algorithms [Details]
B. Petek
  • ESANN 1994 - A discriminative HCNN modeling [Details]
Tino Peter
  • ESANN 2012 - Process Mining in Non-Stationary Environments [Details]
  • ESANN 2012 - Short Term Memory Quantifications in Input-Driven Linear Dynamical Systems [Details]
  • ESANN 2012 - Theory of Input Driven Dynamical Systems [Details]
Jan Peters
  • ESANN 2007 - Applying the Episodic Natural Actor-Critic Architecture to Motor Primitive Learning [Details]
  • ESANN 2008 - Learning Inverse Dynamics: a Comparison [Details]
  • ESANN 2008 - Model-Based Reinforcement Learning with Continuous States and Actions [Details]
Gabriele Peters
  • ESANN 2014 - Supporting GNG-based clustering with local input space histograms [Details]
G. Peters
  • ESANN 1997 - Object recognition with banana wavelets [Details]
  • ESANN 2002 - Learning sparse representations of three-dimensional objects [Details]
L. Peters
  • ESANN 2002 - Connectionist models investigating representations formed in the sequential generation of characters [Details]
  • ESANN 2002 - Why will rat's go where rats will not? [Details]
Karl-Magnus Petersson
  • No papers found
Robert Petit
  • ESANN 2013 - Read classification for next generation sequencing [Details]
Nicolai Petkov
  • ESANN 2006 - Classification of Boar Sperm Head Images using Learning Vector Quantization [Details]
  • ESANN 2009 - Adaptive Metrics for Content Based Image Retrieval in Dermatology [Details]
  • ESANN 2018 - Globular Cluster Detection in the Gaia Survey [Details]
Teodora Petrisor
  • ESANN 2024 - Towards Contrail Mitigation through Robust and Frugal AI-Driven Data Exploitation [Details]
A. Petrolini
  • ESANN 1999 - Support vector machines vs multi-layer perceptrons in particle identification [Details]
Vahan Petrosyan
  • ESANN 2017 - Viral initialization for spectral clustering [Details]
Riccardo Petrucci
  • ESANN 2026 - Model Selection Hijacking Adversarial Attack [Details]
Tomáš Pevný
  • ESANN 2026 - Distillation of a tractable model from the VQ-VAE [Details]
Clément Peyrard
  • ESANN 2016 - Boosting face recognition via neural Super-Resolution [Details]
Thomas Pfaff
  • ESANN 2026 - Evaluation of Rashomon Sets for the Determination of Stable and Plausible Model Explanations [Details]
Lukas Pfannschmidt
  • ESANN 2017 - Feature Relevance Bounds for Linear Classification [Details]
  • ESANN 2019 - Feature relevance bounds for ordinal regression [Details]
Beat Pfister
  • ESANN 2004 - Speaker verification by means of ANNs [Details]
G. Pfurtscheller
  • ESANN 1995 - Improvement of EEG classification with a subject-specific feature selection [Details]
  • ESANN 1995 - Trimming the inputs of RBF networks [Details]
Tung Pham
  • ESANN 2016 - Fast Support Vector Clustering [Details]
Dinh Nam Pham
  • ESANN 2023 - Disambiguating Signs: Deep Learning-based Gloss-level Classification for German Sign Language by Utilizing Mouth Actions [Details]
Dinh-Tuan Pham
  • ESANN 2006 - Discriminacy of the minimum range approach to blind separation of bounded sources [Details]
Rémy Phan-Ba
  • ESANN 2014 - Machine learning techniques to assess the performance of a gait analysis system [Details]
Markus Philipp
  • ESANN 2018 - Self-learning assembly systems during ramp-up [Details]
Christophe Phillips
  • ESANN 2017 - Support vector components analysis [Details]
Dinh Phung
  • ESANN 2014 - Using Shannon Entropy as EEG Signal Feature for Fast Person Identification [Details]
Justus Piater
  • ESANN 2014 - Joint SVM for Accurate and Fast Image Tagging [Details]
  • ESANN 2015 - Learning missing edges via kernels in partially-known graphs [Details]

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