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
Jens Schreiter
  • ESANN 2015 - Fast greedy insertion and deletion in sparse Gaussian process regression [Details]
Joachim Schreurs
  • ESANN 2018 - Generative Kernel PCA [Details]
Sarah Schröder
  • No papers found
M. Schröder
  • ESANN 2003 - Towards the restoration of hand grasp function of quadriplegic patients based on an artificial neural net controller using peripheral nerve stimulation - an approach [Details]
Sarah Schröder
  • ESANN 2022 - Neural Architecture Search for Sentence Classification with BERT [Details]
Michael Schröder
  • ESANN 2005 - Feature selection for high-dimensional industrial data [Details]
Sarah Schröder
  • ESANN 2025 - Evaluating Concept Discovery Methods for Sensitive Attributes in Language Models [Details]
W. Schubert
  • ESANN 2000 - A neural network architecture for automatic segmentation of fluorescence micrographs [Details]
Ronny Schubert
  • ESANN 2023 - Variants of Neural Gas for Regression Learning [Details]
  • ESANN 2024 - About Vector Quantization and its Privacy in Federated Learning [Details]
  • ESANN 2025 - Integrating Class Relation Knowledge in Probabilistic Learning Vector Quantization [Details]
  • ESANN 2025 - Mitigating the Bias in Data for Fairness Using an Advanced Generalized Learning Vector Quantization Approach -- FA(IR)$^2$MA-GLVQ [Details]
  • ESANN 2025 - Towards Learning Vector Quantization in the Setting of Homomorphic Encryption [Details]
Ronny Schubert
  • ESANN 2021 - The LVQ-based Counter Propagation Network -- an Interpretable Information Bottleneck Approach [Details]
Rene Schueffny
  • ESANN 2010 - A critique of BCM behavior verification for STDP-type plasticity models [Details]
M. Schuemie
  • ESANN 1999 - Information retrieval systems using an associative conceptual space [Details]
Rene Schüffny
  • ESANN 2009 - On the routing complexity of neural network models - Rent's Rule revisited [Details]
Björn Schuller
  • ESANN 2023 - Multimodal Recognition of Valence, Arousal and Dominance via Late-Fusion of Text, Audio and Facial Expressions [Details]
Tanja Schultz
  • ESANN 2018 - interpretation of convolutional neural networks for speech regression from electrocorticography [Details]
Hannes Schulz
  • ESANN 2010 - Exploiting local structure in stacked Boltzmann machines [Details]
  • ESANN 2012 - Learning Object-Class Segmentation with Convolutional Neural Networks [Details]
  • ESANN 2015 - Depth and height aware semantic RGB-D perception with convolutional neural networks [Details]
Alexander Schulz
  • ESANN 2014 - Relevance Learning for Dimensionality Reduction [Details]
  • ESANN 2015 - Unsupervised Dimensionality Reduction for Transfer Learning [Details]
  • ESANN 2016 - Discriminative dimensionality reduction in kernel space [Details]
  • ESANN 2017 - An EM transfer learning algorithm with applications in bionic hand prostheses [Details]
Alexander Schulz
  • ESANN 2020 - Reservoir memory machines [Details]
  • ESANN 2024 - Noise Robust One-Class Intrusion Detection on Dynamic Graphs [Details]
  • ESANN 2024 - Visualizing and Improving 3D Mesh Segmentation with DeepView [Details]
  • ESANN 2025 - Conceptualizing Concept Drift [Details]
  • ESANN 2025 - Evaluating Concept Discovery Methods for Sensitive Attributes in Language Models [Details]
Denis Schulze
  • ESANN 2011 - Automatic Enhancement of Correspondence Detection in an Object Tracking System [Details]
S. Schünemann
  • ESANN 1996 - A self-organizing map for analysis of high-dimensional feature spaces with clusters of highly differing feature density [Details]
  • ESANN 1999 - A hierarchical self-organizing feature map for analysis of not well separable clusters of different feature density [Details]
D. Schunk
  • ESANN 2001 - Relevance determination in Learning Vector Quantization [Details]
J. Schürmann
  • ESANN 1999 - Dimensionality reduction by local processing [Details]
  • ESANN 1999 - Segmentation-free detection of overtaking vehicles with a two-stage time-delay neural network classifier [Details]
Cornelius Schwarz
  • ESANN 2008 - Direct and inverse solution for a stimulus adaptation problem using SVR [Details]
Lucas Schwarz
  • ESANN 2021 - The LVQ-based Counter Propagation Network -- an Interpretable Information Bottleneck Approach [Details]
H. Schwegler
  • ESANN 1995 - Predicting spike train responses of neuron models [Details]
  • ESANN 1999 - Critical and non-critical avalanche behavior in networks of integrate-and-fire neurons [Details]
  • ESANN 2001 - Extracting motion information using a biologically realistic model retina [Details]
Friedhelm Schwenker
  • ESANN 2004 - Classification of Bioacoustic Time Series by Training a Decision Fusion mapping [Details]
  • ESANN 2008 - Detecting zebra crossings utilizing AdaBoost [Details]
  • ESANN 2008 - Learning to play Tetris applying reinforcement learning methods [Details]
  • ESANN 2008 - Multi-View Forests of Tree-Structured Radial Basis Function Networks Based on Dempster-Shafer Evidence Theory [Details]
  • ESANN 2009 - Echo State networks and Neural network Ensembles to predict Sunspots activity [Details]
  • ESANN 2011 - Training of multiple classifier systems utilizing partially labeled sequential data sets [Details]
  • ESANN 2014 - Selective Neural Network Ensembles in Reinforcement Learning [Details]
  • ESANN 2015 - SMO Lattices for the Parallel Training of Support Vector Machines [Details]
  • ESANN 2017 - Hierarchical Combination of Video Features for Personalised Pain Level Recognition [Details]
Friedhelm Schwenker
  • ESANN 2020 - Automatic Pain Intensity Recognition: Training Set Selection based on Outliers and Centroids [Details]
  • ESANN 2023 - Potential analysis of a Quantum RL controller in the context of autonomous driving [Details]
P. Schyns
  • ESANN 1997 - Aspects of psychological computation in scene and face recognition [Details]
Luca Scionis
  • ESANN 2023 - Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization [Details]
T. Scutt
  • ESANN 1997 - Real neurons in real networks [Details]