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
Rui F. Pinto
  • ESANN 2024 - Leveraging Physics-Informed Neural Networks as Solar Wind Forecasting Models [Details]
David Pinto
  • ESANN 2015 - An affinity matrix approach for structure selection of extreme learning machines [Details]
Maura Pintor
  • ESANN 2021 - Slope: A First-order Approach for Measuring Gradient Obfuscation [Details]
  • ESANN 2023 - Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization [Details]
  • ESANN 2023 - Towards Machine Learning Models that We Can Trust: Testing, Improving, and Explaining Robustness [Details]
Gordon Pipa
  • ESANN 2006 - The combination of STDP and intrinsic plasticity yields complex dynamics in recurrent spiking networks [Details]
Luepol Pipanmaekaporn
  • ESANN 2015 - Designing semantic feature spaces for brain-reading [Details]
S. Piramuthu
  • ESANN 1996 - Neural versus neurofuzzy systems for credit approval [Details]
Giorgio Piras
  • ESANN 2023 - Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization [Details]
Guilherme Pires
  • ESANN 2020 - Variational MIxture of Normalizing Flows [Details]
Gueorgui Pironkov
  • ESANN 2016 - Multi-task learning for speech recognition: an overview [Details]
J. Pizarro
  • ESANN 2000 - A statistical model selection strategy applied to neural networks [Details]
J. Pizarro Junquera
  • ESANN 2002 - A resampling and multiple testing-based procedure for determining the size of a neural network [Details]
  • ESANN 2002 - Noise derived information criterion for model selection [Details]
  • ESANN 2003 - A new Meta Machine Learning (MML) method based on combining non-significant different neural networks [Details]
Stefano Pizzuti
  • ESANN 2010 - Combining back-propagation and genetic algorithms to train neural networks for start-up time modeling in combined cycle power plants [Details]
Albert Pla
  • ESANN 2016 - Bag-of-Steps: predicting lower-limb fracture rehabilitation length [Details]
David Plans
  • ESANN 2017 - ELM Preference Learning for Physiological Data [Details]
Alessio Plebe
  • ESANN 2006 - Learning Visual Invariance [Details]
Paul Plöger
  • ESANN 2019 - Real-time Convolutional Neural Networks for emotion and gender classification [Details]
Marc Plouvin
  • ESANN 2014 - Enhanced NMF initialization using a physical model for pollution source apportionment [Details]
Marco Podda
  • ESANN 2024 - XAI and Bias of Deep Graph Networks [Details]
Marco Podda
  • ESANN 2020 - Biochemical Pathway Robustness Prediction with Graph Neural Networks [Details]
  • ESANN 2023 - Graph Representation Learning [Details]
Ondřej Podsztavek
  • ESANN 2024 - Automatic Miscalibration Diagnosis: Interpreting Probability Integral Transform (PIT) Histograms [Details]
Domonkos Pogány
  • ESANN 2024 - Hyperbolic Metabolite-Disease Association Prediction [Details]
Alessandro Poggiali
  • ESANN 2023 - Quantum Feature Selection with Variance Estimation [Details]
Valentina Poggioni
  • ESANN 2021 - Combining Attack Success Rate and DetectionRate for effective Universal Adversarial Attacks [Details]
Johannes Pohl
  • ESANN 2015 - I see you: on neural networks for indoor geolocation [Details]
Pierre Poitier
  • No papers found
Pierre Poitier
  • ESANN 2022 - Towards Better Transition Modeling in Recurrent Neural Networks: the Case of Sign Language Tokenization [Details]
Ondrej Pokora
  • ESANN 2012 - How regular is neuronal activity? [Details]
D. Pokrajac
  • ESANN 2000 - Distributed clustering and local regression for knowledge discovery in multiple spatial databases [Details]
Adrian Alan Pol
  • ESANN 2023 - Knowledge Distillation for Anomaly Detection [Details]
J. Poland
  • ESANN 2002 - Different criteria for active learning in neural networks: a comparative study [Details]