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
Yohannes Kassahun
  • ESANN 2005 - Efficient reinforcement learning through Evolutionary Acquisition of Neural Topologies [Details]
Marika Kästner
  • ESANN 2012 - Integration of Structural Expert Knowledge about Classes for Classification Using the Fuzzy Supervised Neural Gas [Details]
  • ESANN 2012 - Modified Conn-Index for the evaluation of fuzzy clusterings [Details]
  • ESANN 2013 - A sparse kernelized matrix learning vector quantization model for human activity recognition [Details]
  • ESANN 2013 - Border sensitive fuzzy vector quantization in semi-supervised learning [Details]
  • ESANN 2013 - Processing Hyperspectral Data in Machine Learning [Details]
  • ESANN 2013 - Regularization in relevance learning vector quantization using l1-norms [Details]
Rafael Katopodis
  • ESANN 2021 - Functional Gradient Descent for n-Tuple Regression [Details]
Sebastian Kauschke
  • ESANN 2019 - Beta Distribution Drift Detection for Adaptive Classifiers [Details]
J. Kawala
  • ESANN 1999 - Comparison of Kohonen, scale-invariant and GTM self-organising maps for interpretation of spectral data [Details]
Motoaki Kawanabe
  • ESANN 2004 - Regularizing generalization error estimators: a novel approach to robust model selection [Details]
Motoaki Kawanabe
  • ESANN 2024 - Geometric Deep Learning to Enhance Imbalanced Domain Adaptation in EEG [Details]
Megumi Kawase
  • ESANN 2021 - Emotional Intensity Level Analysis of Speech Emotional Intensity Estimation [Details]
Jing Ke
  • ESANN 2017 - A performance acceleration algorithm of spectral unmixing via subset selection [Details]
V. Kecman
  • ESANN 2003 - On the equality of kernel AdaTron and sequential minimal optimization in classification and regression tasks and alike algorithms for kernel machines [Details]
Vojislav Kecman
  • ESANN 2004 - Bias Term b in SVMs Again [Details]
Balazs Kegl
  • ESANN 2016 - How machine learning won the Higgs boson challenge [Details]
Daniel Keim
  • ESANN 2016 - Human-centered machine learning through interactive visualization: review and open challenges [Details]
  • ESANN 2018 - G-Rap: interactive text synthesis using recurrent neural network suggestions [Details]
Abou KEITA
  • ESANN 2013 - Detection and quantification in real-time polymerase chain reaction [Details]
Vili Kellokumpu
  • ESANN 2018 - Effect of context in swipe gesture-based continuous authentication on smartphones [Details]
Bélise Kenmogne Edith
  • ESANN 2025 - Benchmarking Data Augmentation for Contrastive Learning in Static Sign Language Recognition [Details]
Ken Kennedy
  • ESANN 2017 - Algebraic multigrid support vector machines [Details]
Philip Kenneweg
  • ESANN 2022 - Neural Architecture Search for Sentence Classification with BERT [Details]
Philip Kenneweg
  • No papers found
Tristan Kenneweg
  • ESANN 2025 - JEPA for RL: Investigating Joint-Embedding Predictive Architectures for Reinforcement Learning [Details]
Philip Kenneweg
  • ESANN 2025 - JEPA for RL: Investigating Joint-Embedding Predictive Architectures for Reinforcement Learning [Details]
Jochen Kerdels
  • ESANN 2014 - Supporting GNG-based clustering with local input space histograms [Details]
  • No papers found
Asma Kerkeni
  • ESANN 2021 - CAS-Net: A Novel Coronary Artery Segmentation Neural Network [Details]
Roman Kern
  • ESANN 2018 - Understanding wafer patterns in semiconductor production with variational auto-encoders [Details]
Matthias Kerzel
  • ESANN 2018 - Slowness-based neural visuomotor control with an Intrinsically motivated Continuous Actor-Critic [Details]
Matthias Kerzel
  • ESANN 2021 - Pruning Neural Networks with Supermasks [Details]
Lounis Kessal
  • ESANN 2008 - Neural network hardware architecture for pattern recognition in the HESS2 project [Details]
Viktor Kessler
  • ESANN 2017 - Hierarchical Combination of Video Features for Personalised Pain Level Recognition [Details]
Janis Keuper
  • ESANN 2021 - Sample efficient localization and stage prediction with autoencoders [Details]