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
Kwabena Boahen
  • ESANN 2023 - Energy-efficient detection of a spike sequence [Details]
Levy Boccato
  • ESANN 2013 - Error entropy criterion in echo state network training [Details]
Ronald Böck
  • ESANN 2013 - Auto-encoder pre-training of segmented-memory recurrent neural networks [Details]
Botond Bocsi
  • ESANN 2009 - Dirichlet process-based component detection in state-space models [Details]
Joschka Bödecker
  • ESANN 2017 - Predicting Time Series with Space-Time Convolutional and Recurrent Neural Networks [Details]
M. Boden
  • ESANN 2002 - Generalization by structural properties from sparse nested symbolic data [Details]
Zalán Bodó
  • ESANN 2021 - Validating static call graph-based malware signatures using community detection methods [Details]
Zalán Bodó
  • ESANN 2013 - Linear spectral hashing [Details]
Zalán-Péter Bodó
  • ESANN 2014 - Augmented hashing for semi-supervised scenarios [Details]
Rene te Boeckhorst
  • ESANN 2006 - Using sampling methods to improve binding site predictions [Details]
Joschka Boedecker
  • ESANN 2009 - Studies on reservoir initialization and dynamics shaping in echo state networks [Details]
  • ESANN 2018 - Controlling biological neural networks with deep reinforcement learning [Details]
H.-J. Boehme
  • ESANN 1995 - An episodic knowledge base for object understanding [Details]
  • ESANN 1999 - Visual-based posture recognition using hybrid neural networks [Details]
Andre Boen
  • ESANN 2012 - EMFit based Ultrasonic Phased Arrays with evolved Weights for Biomimetic Target Localization [Details]
Wim Boes
  • ESANN 2020 - On the long-term learning ability of LSTM LMs [Details]
Jérémie Bogaert
  • ESANN 2023 - Fine-tuning is not (always) overfitting artifacts [Details]
  • ESANN 2024 - Does a Reduced Fine-Tuning Surface Impact the Stability of the Explanations of LLMs? [Details]
Martin Bogdan
  • ESANN 2023 - Is Boredom an Indicator on the way to Singularity of Artificial Intelligence? Hypotheses as Thought-Provoking Impulse [Details]
  • ESANN 2023 - Sleep analysis in a CLIS patient using soft-clustering: a case study [Details]
  • ESANN 2025 - A Pipeline based on Differential Evolution for Tuning Parameters of Synaptic Dynamics Models [Details]
  • ESANN 2025 - The Regulatory Character of Boredom in AI - Towards a Self-Regulating System based on Spiking Neural Networks [Details]
  • ESANN 2025 - The Reinforced Liquid State Machine: A New Training Architecture for Spiking Neural Networks [Details]
M. Bogdan
  • ESANN 2001 - Detection of cluster in Self-Organizing Maps for controlling a prostheses using nerve signals [Details]
  • 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]
Martin Bogdan
  • ESANN 2005 - Feature selection for high-dimensional industrial data [Details]
  • ESANN 2006 - Artificial neural networks and machine learning for man-machine-interfaces - processing of nervous signals [Details]
  • ESANN 2008 - Direct and inverse solution for a stimulus adaptation problem using SVR [Details]
  • ESANN 2010 - Predicting spike-timing of a thalamic neuron using a stochastic synaptic model [Details]
  • ESANN 2011 - Classifying mental states with machine learning algorithms using alpha activity decline [Details]
  • ESANN 2012 - One Class SVM and Canonical Correlation Analysis increase performance in a c-VEP based Brain-Computer Interface (BCI) [Details]
  • ESANN 2013 - Decoding stimulation intensity from evoked ECoG activity using support vector regression [Details]
  • ESANN 2013 - Efficient prediction of x-axis intercepts of discrete impedance spectra [Details]
  • ESANN 2017 - Collaborative filtering with neural networks [Details]
  • ESANN 2018 - Neural Networks for Implicit Feedback Datasets [Details]
Martin Böhm
  • ESANN 2021 - An Algorithmic Approach to Establish a Lower Bound for the Size of Semiring Neural Networks [Details]
Katrin Sophie Bohnsack
  • ESANN 2022 - Efficient classification learning of biochemical structured data by means of relevance weighting for sensoric response features [Details]
  • ESANN 2023 - Sparse Nyström Approximation for Non-Vectorial Data Using Class-informed Landmark Selection [Details]
Katrin Sophie Bohnsack
  • No papers found
S. M. Bohte
  • ESANN 2000 - SpikeProp: backpropagation for networks of spiking neurons [Details]
Sander Bohte
  • ESANN 2014 - Learning resets of neural working memory [Details]
  • ESANN 2014 - Spiking AGREL [Details]
  • ESANN 2014 - Spiking Neural Networks: Principles and Challenges [Details]
S.M. Bohte
  • ESANN 2002 - Modeling efficient conjunction detection with spiking neural networks [Details]
Luis Eduardo Boiko
  • ESANN 2018 - Adaptive random forests for data stream regression [Details]
Hugo Boisaubert
  • ESANN 2025 - Investigating four deep learning approaches as candidates for unified models in time series forecasting and event prediction: application in anesthesia training [Details]
J.-M. Boite
  • ESANN 1999 - Development of a French speech recognizer using a hybrid HMM/MLP system [Details]
T. Bojer
  • ESANN 2001 - Relevance determination in Learning Vector Quantization [Details]
  • ESANN 2003 - Monitoring technical systems with prototype based clustering [Details]
Gianluca Boleto
  • ESANN 2021 - In-Station Train Movements Prediction: from Shallow to Deep Multi Scale Models [Details]
D. Bollé
  • ESANN 1993 - Mixture states in Potts neural networks [Details]
  • ESANN 1993 - Paralell dynamics of extremely diluted neural networks [Details]