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
Andrey Gritsenko
  • ESANN 2017 - Advanced query strategies for Active Learning with Extreme Learning Machines [Details]
Christoph Groenegress
  • ESANN 2009 - Brain Computer Interface for Virtual Reality Control [Details]
Marta Gromicho
  • ESANN 2024 - Deep Temporal Consensus Clustering for Patient Stratification in Amyotrophic Lateral Sclerosis [Details]
Martin Gromniak
  • ESANN 2026 - Towards Learning a Generalizable 3D Scene Representation from 2D Observations [Details]
Sonja Gronroos
  • ESANN 2023 - Knowledge Distillation for Anomaly Detection [Details]
Perry Groot
  • ESANN 2009 - Multi-task Preference learning with Gaussian Processes [Details]
Claudius Gros
  • ESANN 2009 - Stimulus processing and unsupervised learning in autonomously active recurrent networks [Details]
  • ESANN 2015 - An objective function for self-limiting neural plasticity rules. [Details]
  • ESANN 2017 - Complex activity patterns generated by short-term synaptic plasticity [Details]
Wolfgang Groß
  • ESANN 2017 - Predicting Time Series with Space-Time Convolutional and Recurrent Neural Networks [Details]
Claudius Gros
  • No papers found
Horst-Michael Groß
  • ESANN 2012 - Effects of noise-reduction on neural function approximation [Details]
Claudius Gros
  • ESANN 2022 - Size Scaling in Self-Play Reinforcement Learning [Details]
Lars Frank Große
  • ESANN 2009 - The Use of ANN for Turbo Engine Applications [Details]
Lars Große
  • ESANN 2010 - Approximation of chemical reaction rates in turbulent combustion simulation [Details]
Alexander Gross
  • ESANN 2008 - Learning to play Tetris applying reinforcement learning methods [Details]
Dennis Gross
  • ESANN 2024 - Safety-Oriented Pruning and Interpretation of Reinforcement Learning Policies [Details]
Horst-Michael Gross
  • ESANN 2009 - Forward feature selection using Residual Mutual Information [Details]
  • ESANN 2013 - Evolutionary computation based system decomposition with neural networks [Details]
H.-M. Gross
  • ESANN 1995 - An episodic knowledge base for object understanding [Details]
  • ESANN 1998 - Perception and action selection by anticipation of sensorimotor consequences [Details]
  • ESANN 1999 - Visual-based posture recognition using hybrid neural networks [Details]
Markus Gross
  • ESANN 2025 - Expressivity vs. Generalization in Quantum Kernel Methods [Details]
Lars Grosse
  • No papers found
Silvia Grosso
  • ESANN 2026 - When Curvature Counts: Hyperbolic Geometry in Prototype-Based Image Classification [Details]
R. Grothmann
  • ESANN 2002 - Undershooting: modeling dynamical systems by time grid refinements [Details]
  • ESANN 2002 - Yield curve forecasting by error correction neural networks and partial learning [Details]
F. Gruau
  • ESANN 1993 - A learning and pruning algorithm for genetic Boolean neural networks [Details]
  • ESANN 1993 - A mental problem for the solution of the direct and inverse kinematic problem [Details]
Peter Gruber
  • ESANN 2004 - Separability of analytic postnonlinear blind source separation with bounded sources [Details]
A. Grumbach
  • ESANN 1997 - d-NARMA neural networks: a connectionist extension of ARARMA models [Details]
André Grüning
  • ESANN 2014 - Classifying Patterns in a Spiking Neural Network [Details]
  • ESANN 2014 - Spiking Neural Networks: Principles and Challenges [Details]
Andre Gruning
  • ESANN 2010 - Modelling the McGurk effect [Details]
Vadym Gryshchuk
  • ESANN 2021 - Lifelong Learning from Event-based Data [Details]
Przemys{\l}aw Grzybowski
  • ESANN 2020 - Understanding and improving unsupervised training of Boltzman machines [Details]
Frederico Gualberto Ferreira Coelho
  • ESANN 2025 - Enhancing Image Classification in Quantum Computing: A Study on Preprocessing Techniques and Qubit Limitations [Details]
  • ESANN 2026 - Scaling up graph-based classifiers with a divide and conquer approach [Details]
Huang Guang-Bin
  • ESANN 2010 - Random search enhancement of error minimized extreme learning machine [Details]