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
Ondřej Podsztavek
  • ESANN 2024 - Automatic Miscalibration Diagnosis: Interpreting Probability Integral Transform (PIT) Histograms [Details]
Domonkos Pogány
  • ESANN 2024 - Hyperbolic Metabolite-Disease Association Prediction [Details]
  • ESANN 2025 - Hyperbolic representation learning in multi-layer tissue networks [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
  • ESANN 2022 - Towards Better Transition Modeling in Recurrent Neural Networks: the Case of Sign Language Tokenization [Details]
Pierre Poitier
  • ESANN 2025 - Benchmarking Data Augmentation for Contrastive Learning in Static Sign Language Recognition [Details]
Pierre Poitier
  • No papers found
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]
Mirko Polato
  • ESANN 2022 - Bayes Point Rule Set Learning [Details]
  • ESANN 2024 - FedHP: Federated Learning with Hyperspherical Prototypical Regularization [Details]
  • ESANN 2024 - Machine learning in distributed, federated and non-stationary environments - recent trends [Details]
  • ESANN 2024 - Vision Language Models as Policy Learners in Reinforcement Learning Environments [Details]
Mirko Polato
  • ESANN 2021 - Privacy-Preserving Kernel Computation For Vertically Partitioned Data [Details]
Mirko Polato
  • ESANN 2016 - Kernel based collaborative filtering for very large scale top-N item recommendation [Details]
  • ESANN 2018 - Boolean kernels for interpretable kernel machines [Details]
  • ESANN 2018 - The minimum effort maximum output principle applied to Multiple Kernel Learning [Details]
Mirko Polato
  • No papers found
Arturs Polis
  • ESANN 2021 - A Relational Model for One-Shot Classification [Details]
Kai Polsterer
  • ESANN 2014 - Speedy greedy feature selection: Better redshift estimation via massive parallelism [Details]
  • ESANN 2015 - Autoencoding time series for visualisation [Details]
Kai Lars Polsterer
  • ESANN 2016 - Parallelized rotation and flipping INvariant Kohonen maps (PINK) on GPUs [Details]
  • ESANN 2017 - Uncertain photometric redshifts via combining deep convolutional and mixture density networks [Details]
Kai L. Polsterer
  • ESANN 2024 - Automatic Miscalibration Diagnosis: Interpreting Probability Integral Transform (PIT) Histograms [Details]
  • ESANN 2024 - Positive and Scale Invariant Gaussian Process Latent Variable Model for Astronomical Spectra [Details]
Marios Polycarpou
  • ESANN 2024 - Self-Supervised Learning from Incrementally Drifting Data Streams [Details]
Georg Pölzlbauer
  • ESANN 2005 - Graph projection techniques for Self-Organizing Maps [Details]
H. Pomares
  • ESANN 1998 - What are the main factors involved in the design of a Radial Basis Function Network? [Details]
Héctor Pomares
  • ESANN 2004 - MultiGrid-Based Fuzzy Systems for Time Series: Forecasting: Overcoming the curse of dimensionality [Details]
  • ESANN 2009 - Applying Mutual Information for Prototype or Instance Selection in Regression Problems [Details]
Héctor Pomares
  • ESANN 2004 - Soft-computing techniques for time series forecasting [Details]
Filippo Pompili
  • ESANN 2013 - ONP-MF: An Orthogonal Nonnegative Matrix Factorization Algorithm with Application to Clustering [Details]
Tossapol Pomsuwan
  • ESANN 2020 - Adapting Random Forests to Cope with Heavily Censored Datasets in Survival Analysis [Details]
A. Poncet
  • ESANN 1998 - Selecting among candidate basis functions by crosscorrelations [Details]
Massimiliano Pontil
  • ESANN 2019 - PAC-Bayes and Fairness: Risk and Fairness Bounds on Distribution Dependent Fair Priors [Details]
M. Pontil
  • ESANN 1999 - From regression to classification in support vector machines [Details]
  • ESANN 1999 - Support vector machines vs multi-layer perceptrons in particle identification [Details]
  • ESANN 2003 - On different ensembles of kernel machines [Details]
  • ESANN 2003 - Reproducing kernels and regularization methods in machine learning [Details]