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
Marco Corneli
  • ESANN 2015 - Exact ICL maximization in a non-stationary time extension of latent block model for dynamic networks [Details]
Marco Corneli
  • ESANN 2022 - Deep latent position model for node clustering in graphs [Details]
Dan Cornford
  • ESANN 2009 - A variational radial basis function approximation for diffusion processes [Details]
Francesco Corona
  • ESANN 2006 - Determination of the Mahalanobis matrix using nonparametric noise estimations [Details]
  • ESANN 2007 - Nearest Neighbor Distributions and Noise Variance Estimation [Details]
  • ESANN 2008 - Linear Projection based on Noise Variance Estimation - Application to Spectral Data [Details]
  • ESANN 2008 - Using the Delta Test for Variable Selection [Details]
  • ESANN 2011 - Locating Anomalies Using Bayesian Factorizations and Masks [Details]
Barbara Toniella Corradini
  • No papers found
A. Corradini
  • ESANN 1999 - Visual-based posture recognition using hybrid neural networks [Details]
Barbara Toniella Corradini
  • ESANN 2022 - A Deep Learning approach for oocytes segmentation and analysis [Details]
João Gabriel Corrêa Krüger
  • ESANN 2022 - A Machine Learning Approach for School Dropout Prediction in Brazil [Details]
João Gabriel Corrêa Krüger
  • No papers found
André Correia
  • ESANN 2023 - DEFENDER: DTW-Based Episode Filtering Using Demonstrations for Enhancing RL Safety [Details]
Marie-Constance Corsi
  • ESANN 2024 - Deep Riemannian Neural Architectures for Domain Adaptation in Burst cVEP-based Brain Computer Interface [Details]
Marc-Michel Corsini
  • ESANN 2004 - Computational model of amygdala network supported by neurobiological data [Details]
Iliana Isabel Cortés Pérez
  • ESANN 2024 - Positive and Scale Invariant Gaussian Process Latent Variable Model for Astronomical Spectra [Details]
P. Cortez
  • ESANN 2001 - Lamarckian training of feedforward neural networks [Details]
  • ESANN 2003 - Adaptive Learning in Changing Environments [Details]
Carlos Cosenza
  • ESANN 2020 - Interpretation of Model Agnostic Classifiers via Local Mental Images [Details]
M. Cosnard
  • ESANN 1993 - Probabilistic decision trees ans multilayered perceptrons [Details]
J. Cosp
  • ESANN 2001 - A microelectronic implementation of a bioinspired analog matrix for object segmentation of a visual scene [Details]
Jordi Cosp
  • ESANN 2004 - BIOSEG: a bioinspired vlsi analog system for image segmentation [Details]
Andrea Cossu
  • ESANN 2021 - Continual Learning with Echo State Networks [Details]
  • ESANN 2022 - Continual Learning for Human State Monitoring [Details]
  • ESANN 2023 - A Protocol for Continual Explanation of SHAP [Details]
Andrea Cossu
  • ESANN 2024 - Enhancing Echo State Networks with Gradient-based Explainability Methods [Details]
  • ESANN 2024 - Towards Deep Continual Workspace Monitoring: Performance Evaluation of CL Strategies for Object Detection in Working Sites [Details]
  • ESANN 2025 - Don't drift away: Advances and Applications of Streaming and Continual Learning [Details]
  • ESANN 2025 - Replay-free Online Continual Learning with Self-Supervised MultiPatches [Details]
  • ESANN 2026 - Random Unicycle Network (RUN!): supercharging harmonic oscillator networks via non-holonomic constraints [Details]
Marcelo Costa
  • 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 2015 - Training Multi-Layer Perceptron with Multi-Objective Optimization and Spherical Weights Representation [Details]
F. Costa
  • ESANN 1999 - A topological transformation for hidden recursive modelsarchitecture networks [Details]
M. Costa
  • ESANN 1994 - Combining multi-layer perceptrons in classification problems [Details]
Nuno Costa
  • ESANN 2024 - Leveraging Physics-Informed Neural Networks as Solar Wind Forecasting Models [Details]
Fabrizio Costa
  • ESANN 2016 - RNAsynth: constraints learning for RNA inverse folding. [Details]
  • ESANN 2017 - Fast hyperparameter selection for graph kernels via subsampling and multiple kernel learning [Details]
  • ESANN 2017 - The Conjunctive Disjunctive Node Kernel [Details]
  • ESANN 2019 - Progress Towards Graph Optimization: Efficient Learning of Vector to Graph Space Mappings [Details]
Marta Costa-jussa
  • ESANN 2017 - Bridging deep and kernel methods [Details]
Filippo Costanti
  • No papers found
Filippo Costanti
  • ESANN 2022 - A Deep Learning approach for oocytes segmentation and analysis [Details]
Yannis Cotronis
  • ESANN 2017 - A Deep Q-Learning Agent for L-Game with Variable Batch Training [Details]
M. Cottrell
  • ESANN 1993 - Time series and neural: a statistical method for weight elimination [Details]
  • ESANN 1994 - Two or three things that we know about the Kohonen algorithm [Details]
  • ESANN 1995 - Multiple correspondence analysis of a crosstabulations matrix using the Kohonen algorithm [Details]
  • ESANN 1996 - A Kohonen map representation to avoid misleading interpretations [Details]
  • ESANN 1997 - Kohonen maps versus vector quantization for data analysis [Details]
  • ESANN 1997 - New criterion of identification in the multilayered perceptron modelling [Details]
  • ESANN 1997 - Self organizing map for adaptive non-stationary clustering: some experimental results on color quantization of image sequences [Details]
  • ESANN 1998 - Forecasting time-series by Kohonen classification [Details]
  • ESANN 1999 - Using the Kohonen algorithm for quick initialization of Simple Competitive Learning algorithm [Details]
  • ESANN 2000 - Bootstrap for neural model selection [Details]
  • ESANN 2000 - Bootstrapping Self-Organizing Maps to assess the statistical significance of local proximity [Details]
  • ESANN 2001 - Some known facts about financial data [Details]
  • ESANN 2002 - Advantages and drawbacks of the Batch Kohonen algorithm [Details]
  • ESANN 2003 - Analyzing surveys using the Kohonen algorithm [Details]