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
Massoud Babaie-Zadeh
  • ESANN 2006 - Semi-Blind Approaches for Source Separation and Independent component Analysis [Details]
Madina Babazhanova
  • ESANN 2021 - Geometric Probing of Word Vectors [Details]
Rohit Babbar
  • ESANN 2020 - Why state-of-the-art deep learning barely works as good as a linear classifier in extreme multi-label text classification [Details]
Rohit Babbar
  • ESANN 2019 - A Simple and Effective Scheme for Data Pre-processing in Extreme Classification [Details]
Davide Bacciu
  • ESANN 2012 - Input-Output Hidden Markov Models for trees [Details]
  • ESANN 2015 - ESNigma: efficient feature selection for echo state networks [Details]
  • ESANN 2016 - A reservoir activation kernel for trees [Details]
  • ESANN 2017 - ELM Preference Learning for Physiological Data [Details]
  • ESANN 2018 - Bioinformatics and medicine in the era of deep learning [Details]
  • ESANN 2018 - Mixture of Hidden Markov Model as Tree Encoder [Details]
  • ESANN 2019 - Detecting Black-box Adversarial Examples through Nonlinear Dimensionality Reduction [Details]
  • ESANN 2019 - Graph generation by sequential edge prediction [Details]
  • ESANN 2019 - Societal Issues in Machine Learning: When Learning from Data is Not Enough [Details]
Davide Bacciu
  • ESANN 2021 - Calliope - A Polyphonic Music Transformer [Details]
  • ESANN 2021 - Inductive learning for product assortment graph completion [Details]
  • ESANN 2022 - Continual Incremental Language Learning for Neural Machine Translation [Details]
  • ESANN 2022 - Continual Learning for Human State Monitoring [Details]
  • ESANN 2022 - Federated Adaptation of Reservoirs via Intrinsic Plasticity [Details]
  • ESANN 2022 - Modular Representations for Weak Disentanglement [Details]
  • ESANN 2023 - A Protocol for Continual Explanation of SHAP [Details]
  • ESANN 2023 - A Tropical View of Graph Neural Networks [Details]
  • ESANN 2023 - Graph Representation Learning [Details]
  • ESANN 2023 - Hidden Markov Models for Temporal Graph Representation Learning [Details]
  • ESANN 2023 - Improving Fairness via Intrinsic Plasticity in Echo State Networks [Details]
  • ESANN 2024 - ADLER - An efficient Hessian-based strategy for adaptive learning rate [Details]
  • ESANN 2024 - Enhancing Echo State Networks with Gradient-based Explainability Methods [Details]
  • ESANN 2024 - Generalizing Convolution to Point Clouds [Details]
  • ESANN 2024 - Large-Scale Continuous Structure Learning from Time-Series Data [Details]
  • ESANN 2024 - Sequential Continual Pre-Training for Neural Machine Translation [Details]
  • ESANN 2025 - Don't drift away: Advances and Applications of Streaming and Continual Learning [Details]
  • ESANN 2025 - Foundation and Generative Models for Graphs [Details]
  • ESANN 2025 - Generalized Stochastic Pooling [Details]
  • ESANN 2025 - Towards Adaptive and Stable Compositional Assemblies of Recurrent Neural Network Modules [Details]
  • ESANN 2025 - Towards Efficient Molecular Property Optimization with Graph Energy Based Models [Details]
Davide Bacciu
  • ESANN 2020 - Biochemical Pathway Robustness Prediction with Graph Neural Networks [Details]
  • ESANN 2020 - Perplexity-free Parametric t-SNE [Details]
  • ESANN 2020 - Tensor Decompositions in Deep Learning [Details]
  • ESANN 2020 - Tensor Decompositions in Recursive Neural Networks for Tree-Structured Data [Details]
  • ESANN 2020 - Theoretically Expressive and Edge-aware Graph Learning [Details]
  • ESANN 2021 - Continual Learning with Echo State Networks [Details]
  • ESANN 2021 - Deep learning for graphs [Details]
  • ESANN 2022 - Deep Learning for Graphs [Details]
  • ESANN 2023 - Communication-Efficient Ridge Regression in Federated Echo State Networks [Details]
  • ESANN 2024 - Informed Machine Learning for Complex Data [Details]
Moez Baccouche
  • ESANN 2016 - Boosting face recognition via neural Super-Resolution [Details]
P. Bach-y-Rita
  • ESANN 2000 - Nonsynaptically connected neural nets [Details]
Andreas Backhaus
  • ESANN 2025 - Machine Learning on Smartphone-Captured Diffraction Data [Details]
Andreas Backhaus
  • ESANN 2010 - Validation of unsupervised clustering methods for leaf phenotype screening [Details]
  • ESANN 2012 - Classifying Scotch Whisky from near-infrared Raman spectra with a Radial Basis Function Network with Relevance Learning [Details]
  • ESANN 2012 - Hardware accelerated real time classification of hyperspectral imaging data for coffee sorting [Details]
  • ESANN 2013 - Processing Hyperspectral Data in Machine Learning [Details]
  • ESANN 2019 - Transfer Learning for transferring machine-learning based models among hyperspectral sensors [Details]
Rolf Backofen
  • ESANN 2019 - Progress Towards Graph Optimization: Efficient Learning of Vector to Graph Space Mappings [Details]
Komail Badami
  • ESANN 2014 - Context- and cost-aware feature selection in ultra-low-power sensor interfaces [Details]
S. Badel
  • ESANN 2003 - VLSI Realization of a Two-Dimensional Hamming Distance Comparator ANN for Image Processing Applications [Details]
fouad Badran
  • ESANN 2004 - Visualization and classification with categorical topological map [Details]
  • ESANN 2005 - Mixed Topological Map [Details]
F. Badran
  • ESANN 1997 - Probabilistic self organized map - application to classification [Details]
  • ESANN 2000 - Topological map for binary data [Details]
Bettina Baeßler
  • ESANN 2024 - On the Stability of Neural Segmentation in Radiology [Details]
Nader Bagherzadeh
  • ESANN 2019 - Design of Power-Efficient FPGA Convolutional Cores with Approximate Log Multiplier [Details]
M. Baglietto
  • ESANN 1998 - Neural networks for the solution of information-distributed optimal control problems [Details]
Roger Bagué-Masanés
  • ESANN 2022 - The role of feature selection in personalized recommender systems [Details]
Roger Bagué-Masanés
  • No papers found
Gabor Bagyo
  • ESANN 2023 - Segmentation and Analysis of Lumbar Spine MRI Scans for Vertebral Body Measurements [Details]
Denis Baheux
  • ESANN 2014 - Towards an effective multi-map self organizing recurrent neuronal network [Details]
Ali Bahramisharif
  • ESANN 2009 - Exploring the impact of alternative feature representations on BCI classification [Details]
Alina Elena Baia
  • ESANN 2021 - Combining Attack Success Rate and DetectionRate for effective Universal Adversarial Attacks [Details]
Stephan Baier
  • ESANN 2017 - Attention-based Information Fusion using Multi-Encoder-Decoder Recurrent Neural Networks [Details]
Rauf Baig
  • ESANN 2004 - Spatial-Temporal artificial neurons applied to online cursive handwritten recognition [Details]
Laurent Baijot
  • ESANN 2019 - MAP best performances prediction for endurance runners [Details]
Daniele Bajoni
  • ESANN 2020 - An quantum algorithm for feedforward neural networks tested on existing quantum hardware [Details]
Jelle Bakker
  • ESANN 2019 - Efficient learning of email similarities for customer support [Details]