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
Carlo Ricciardi
  • ESANN 2025 - A Model of Memristive Nanowire Neuron for Recurrent Neural Networks [Details]
  • ESANN 2026 - Memristive-Friendly Hadamard Reservoirs [Details]
Giuseppe Riccio
  • ESANN 2018 - stellar formation rates in galaxies using machine learning models [Details]
F.M. Richardson
  • ESANN 2002 - Connectionist models investigating representations formed in the sequential generation of characters [Details]
Daniel Richardson
  • ESANN 2005 - linear algebra for time series of spikes [Details]
Frederieke Richert
  • ESANN 2023 - Layered Neural Networks with GELU Activation, a Statistical Mechanics Analysis [Details]
  • ESANN 2024 - On-line Learning Dynamics in Layered Neural Networks with Arbitrary Activation Functions [Details]
  • ESANN 2025 - The Role of the Learning Rate in Layered Neural Networks with ReLU Activation Function [Details]
P. Riddell
  • ESANN 1999 - Regularization in oculomotor adaptation [Details]
P. M. Riddell
  • ESANN 2000 - Regularization in oculomotor control [Details]
Sandro Ridella
  • ESANN 2010 - Maximal Discrepancy for Support Vector Machines [Details]
  • ESANN 2011 - Maximal Discrepancy vs. Rademacher Complexity for error estimation [Details]
  • ESANN 2012 - Structural Risk Minimization and Rademacher Complexity for Regression [Details]
  • ESANN 2012 - The `K' in K-fold Cross Validation [Details]
  • ESANN 2013 - A Learning Machine with a Bit-Based Hypothesis Space [Details]
  • ESANN 2014 - Learning with few bits on small-scale devices: From regularization to energy efficiency [Details]
  • ESANN 2016 - Tuning the Distribution Dependent Prior in the PAC-Bayes Framework based on Empirical Data [Details]
  • ESANN 2017 - Generalization Performances of Randomized Classifiers and Algorithms built on Data Dependent Distributions [Details]
  • ESANN 2018 - Local Rademacher Complexity Machine [Details]
Sandro Ridella
  • ESANN 2020 - Improving the Union Bound: a Distribution Dependent Approach [Details]
  • ESANN 2021 - The Benefits of Adversarial Defence in Generalisation [Details]
  • ESANN 2022 - Do We Really Need a New Theory to Understand the Double-Descent? [Details]
  • ESANN 2023 - Towards Randomized Algorithms and Models that We Can Trust: a Theoretical Perspective [Details]
  • ESANN 2024 - Informed Machine Learning: Excess Risk and Generalization [Details]
  • ESANN 2025 - Reconciling Grokking with Statistical Learning Theory [Details]
S. Ridella
  • ESANN 1995 - Learning the appropriate representation paradigm by circular processing units [Details]
James Ridgway
  • ESANN 2012 - Hidden Markov models for time series of counts with excess zeros [Details]
Hannes Riechmann
  • ESANN 2012 - Semi-Supervised Neural Gas for Adaptive Brain-Computer Interfaces [Details]
Martin Riedel
  • ESANN 2013 - Regularization in relevance learning vector quantization using l1-norms [Details]
Nick Riedman
  • ESANN 2023 - Energy-efficient detection of a spike sequence [Details]
M. Riedmiller
  • ESANN 1997 - Application of a self-learning controller with continuous control signals based on the DOE-approach [Details]
Martin Riedmiller
  • ESANN 2006 - Reducing policy degradation in neuro-dynamic programming [Details]
  • ESANN 2007 - Reinforcement learning in a nutshell [Details]
  • ESANN 2010 - Deep learning of visual control policies [Details]
  • ESANN 2013 - Optimization of Gaussian process hyperparameters using Rprop [Details]
  • ESANN 2018 - Controlling biological neural networks with deep reinforcement learning [Details]
Ines Rieger
  • ESANN 2020 - Verifying Deep Learning-based Decisions for Facial Expression Recognition [Details]
Sebastian Rieger
  • ESANN 2020 - A Survey of Machine Learning applied to Computer Networks [Details]
Hans-Martin Rieser
  • ESANN 2023 - Potential analysis of a Quantum RL controller in the context of autonomous driving [Details]
  • ESANN 2025 - Encoding hyperspectral data with low-bond dimension quantum tensor networks [Details]
  • ESANN 2025 - Enhancing Machine Learning with Quantum Methods [Details]
  • ESANN 2025 - Expressivity vs. Generalization in Quantum Kernel Methods [Details]
  • ESANN 2025 - Quantum Tensor Network Learning with DMRG [Details]
R. Rifkin
  • ESANN 1999 - From regression to classification in support vector machines [Details]
Carsten Riggelsen
  • ESANN 2012 - learning task relatedness via dirichlet process priors for linear regression models [Details]
G. Rigoll
  • ESANN 1998 - Speech recognition with a new hybrid architecture combining neural networks and continuous HMM [Details]
Davide Rigoni
  • ESANN 2020 - A Systematic Assessment of Deep Learning Models for Molecule Generation [Details]
Davide Rigoni
  • ESANN 2025 - D4: Distance Diffusion for a Truly Equivariant Molecular Design [Details]
  • ESANN 2025 - Foundation and Generative Models for Graphs [Details]
  • ESANN 2026 - Neuro Symbolic AI and Complex Data [Details]
  • ESANN 2026 - Ring-constrained Molecular Graph Generation with Diffusion Models [Details]
Dany Rimez
  • ESANN 2026 - Towards meaningful evaluation of uncertainty-aware segmentation workflows for medical applications [Details]
Arpad Rimmel
  • ESANN 2026 - High Performance, Low Reliability: Uncertainty Benchmarking for Tabular Foundation Models [Details]
Fabio Rinaldi
  • ESANN 2020 - Exploring the feature space of character-level embeddings [Details]
Olli-Pekka Rinta-Koski
  • ESANN 2017 - Prediction of preterm infant mortality with Gaussian process classification [Details]
Annette Rios
  • ESANN 2023 - Multimodal Recognition of Valence, Arousal and Dominance via Late-Fusion of Text, Audio and Facial Expressions [Details]
Mathieu Riou
  • ESANN 2024 - Towards Contrail Mitigation through Robust and Frugal AI-Driven Data Exploitation [Details]