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
Sven Haase
  • ESANN 2010 - Divergence based Learning Vector Quantization [Details]
  • ESANN 2010 - Learning vector quantization for heterogeneous structured data [Details]
  • ESANN 2011 - Mathematical Foundations of the Self Organized Neighbor Embedding (SONE) for Dimension Reduction and Visualization [Details]
Zineb Habbas
  • No papers found
Ben Hack
  • ESANN 2021 - SmoothLRP: Smoothing LRP by Averaging over Stochastic Input Variations [Details]
Christodoulos Hadjichristodoulou
  • ESANN 2024 - Interpreting Hybrid AI through Autodecoded Latent Space Entities [Details]
Muhammad Burhan Hafez
  • ESANN 2018 - Slowness-based neural visuomotor control with an Intrinsically motivated Continuous Actor-Critic [Details]
P. Häfliger
  • ESANN 1999 - Learning a temporal code [Details]
Markus Hagenbuchner
  • ESANN 2005 - Contextual Processing of Graphs using Self-Organizing Maps [Details]
  • ESANN 2007 - "Kernelized" Self-Organizing Maps for Structured Data [Details]
  • ESANN 2008 - Self-Organizing Maps for cyclic and unbounded graphs [Details]
  • ESANN 2011 - Sparsity Issues in Self-Organizing-Maps for Structures [Details]
  • ESANN 2013 - Cost-sensitive cascade graph neural networks [Details]
Markus Hagenbuchner
  • ESANN 2020 - Embedding of FRPN in CNN architecture [Details]
M. Hagenbuchner
  • ESANN 1999 - A benchmark for testing adaptive systems on structured data [Details]
Lukas Hahn
  • ESANN 2019 - Fast and reliable architecture selection for convolutional neural networks [Details]
K. Hahn
  • ESANN 2000 - A neural network approach to adaptive pattern analysis - the deformable feature map [Details]
  • ESANN 2001 - Analysis of dynamic perfusion MRI data by neural networks [Details]
Janne Hahne
  • ESANN 2017 - An EM transfer learning algorithm with applications in bionic hand prostheses [Details]
Carolin Hainke
  • ESANN 2019 - Dynamic fairness - Breaking vicious cycles in automatic decision making [Details]
Hannu Häkkinen
  • ESANN 2021 - Orientation Adaptive Minimal Learning Machine for Directions of Atomic Forces [Details]
E. Halasz
  • ESANN 2000 - An optimization neural network model with time-dependent and lossy dynamics [Details]
T. Halawani
  • ESANN 2002 - Use of artificial neural networks process analyzers: a case study [Details]
John Hallam
  • ESANN 2006 - Evolving multi-segment 'super-lamprey' CPG's for increased swimming control [Details]
Rasmus Halland
  • ESANN 2015 - High-School Dropout Prediction Using Machine Learning: A Danish Large-scale Study [Details]
Sandra Halscheidt
  • ESANN 2023 - Segmentation and Analysis of Lumbar Spine MRI Scans for Vertebral Body Measurements [Details]
Valentin Hamaide
  • ESANN 2021 - Transfer learning in Bayesian optimization for the calibration of a beam line in proton therapy [Details]
Joonas Hämäläinen
  • ESANN 2016 - Initialization of big data clustering using distributionally balanced folding [Details]
  • ESANN 2018 - Scalable robust clustering method for large and sparse data [Details]
Joonas Hämäläinen
  • ESANN 2020 - Problem Transformation Methods with Distance-Based Learning for Multi-Target Regression [Details]
  • ESANN 2021 - Instance-Based Multi-Label Classification via Multi-Target Distance Regression [Details]
  • ESANN 2021 - Orientation Adaptive Minimal Learning Machine for Directions of Atomic Forces [Details]
  • ESANN 2023 - Feature Selection for Multi-label Classification with Minimal Learning Machine [Details]
Leila Hamdad
  • No papers found
Rawaa Hamdi
  • ESANN 2021 - CAS-Net: A Novel Coronary Artery Segmentation Neural Network [Details]
Fatma Hamdi
  • ESANN 2010 - Consensus clustering by graph based approach [Details]
Freddie C. Hamdy
  • ESANN 2005 - Artificial intelligence techniques for the prediction of bladder cancer progression [Details]
Victor Hamer
  • ESANN 2020 - Joint optimization of predictive performance and selection stability [Details]
Yasir Hamid
  • ESANN 2017 - Large-scale nonlinear dimensionality reduction for network intrusion detection [Details]
Barbara Hammer
  • ESANN 2004 - Neural methods for non-standard data [Details]
Barbara Hammer
  • ESANN 2020 - Efficient computation of counterfactual explanations of LVQ models [Details]
  • ESANN 2020 - Locally Adaptive Nearest Neighbors [Details]
  • ESANN 2020 - Sparse Metric Learning in Prototype-based Classification [Details]
  • ESANN 2021 - Application of Graph Convolutions in a Lightweight Model for Skeletal Human Motion Forecasting [Details]
  • ESANN 2021 - Federated Learning Vector Quantization [Details]
  • ESANN 2021 - Machine Learning for Measuring and Analyzing Online Social Communications [Details]
  • ESANN 2022 - Federated learning vector quantization for dealing with drift between nodes [Details]
  • ESANN 2022 - From hyperspectral to multispectral sensing – from simulation to reality: A comprehensive approach for calibration model transfer [Details]
  • ESANN 2022 - Improving Zorro Explanations for Sparse Observations with Dense Proxy Data [Details]
  • ESANN 2022 - Model Agnostic Local Explanations of Reject [Details]
  • ESANN 2022 - Neural Architecture Search for Sentence Classification with BERT [Details]
  • ESANN 2023 - On Feature Removal for Explainability in Dynamic Environments [Details]
  • ESANN 2023 - Robust Feature Selection and Robust Training to Cope with Hyperspectral Sensor Shifts [Details]
  • ESANN 2024 - Causes of Rejects in Prototype-based Classification Aleatoric vs. Epistemic Uncertainty [Details]
  • ESANN 2024 - Machine learning in distributed, federated and non-stationary environments - recent trends [Details]
  • ESANN 2024 - Noise Robust One-Class Intrusion Detection on Dynamic Graphs [Details]
  • ESANN 2024 - Self-Supervised Learning from Incrementally Drifting Data Streams [Details]
  • ESANN 2024 - Similarity-Based Zero-Shot Domain Adaptation for Wearables [Details]
  • ESANN 2024 - Trust in Artificial Intelligence: Beyond Interpretability [Details]
  • ESANN 2024 - Visualizing and Improving 3D Mesh Segmentation with DeepView [Details]