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
Kristian Berwanger
  • ESANN 2018 - One-class Autoencoder approach to classify Raman spectra outliers [Details]
Patricia Besson
  • ESANN 2024 - Aeronautic data analysis [Details]
Michael Bevan
  • ESANN 2005 - sparse Bayesian promoter based gene classification [Details]
A. Bevilacqua
  • ESANN 2000 - Automatic detection of clustered microcalcifications in digital mammograms using an SVM classifier [Details]
G.J. Bex
  • ESANN 1994 - Storage capacity of the reversed wedge perceptron with binary connections [Details]
Oliver Beyer
  • ESANN 2013 - DYNG: Dynamic Online Growing Neural Gas for stream data classification [Details]
Jörg Beyer
  • ESANN 2009 - Heterogeneous mixture-of-experts for fusion of locally valid knowledge-based submodels [Details]
Thomas Beznik
  • ESANN 2020 - Deep Learning to Detect Bacterial Colonies for the Production of Vaccines [Details]
Gyan Bhanot
  • ESANN 2017 - Biomedical data analysis in translational research: integration of expert knowledge and interpretable models [Details]
Karan Bhanot
  • ESANN 2021 - Quantifying Resemblance of Synthetic Medical Time-Series [Details]
  • ESANN 2023 - Adversarial Auditing of Machine Learning Models under Compound Shift [Details]
Halandur Nagaraja Bharath
  • ESANN 2016 - Initializing nonnegative matrix factorization using the successive projection algorithm for multi-parametric medical image segmentation [Details]
Anil A Bharath
  • ESANN 2020 - Incorporating Human Priors into Deep Reinforcement Learning for Robotic Control [Details]
Piyush Bhardwaj
  • ESANN 2016 - Efficient low rank approximation via alternating least squares for scalable kernel learning [Details]
B. Bhattacharya
  • ESANN 2003 - Neural Networks and M5 model trees in modeling water level-discharge relationship for an Indian river [Details]
Gaurab Bhattacharya
  • No papers found
Gaurab Bhattacharya
  • ESANN 2022 - Appearance-Context aware Axial Attention for Fashion Landmark Detection [Details]
Chirag Bhuvaneshwara
  • ESANN 2023 - Multimodal Recognition of Valence, Arousal and Dominance via Late-Fusion of Text, Audio and Facial Expressions [Details]
Filippo Maria Bianchi
  • ESANN 2021 - Deep learning for graphs [Details]
  • ESANN 2023 - Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability [Details]
Filippo Maria Bianchi
  • ESANN 2020 - Pyramidal Graph Echo State Networks [Details]
Filippo Maria Bianchi
  • ESANN 2018 - Bidirectional deep-readout echo state networks [Details]
  • ESANN 2018 - Learning compressed representations of blood samples time series with missing data [Details]
Nadia Bianchi-Berthouze
  • ESANN 2013 - A One-Vs-One Classifier Ensemble With Majority Voting for Activity Recognition [Details]
Monica Bianchini
  • ESANN 2004 - Recursive networks for processing graphs with labelled edges [Details]
  • ESANN 2006 - A Cyclostationary Neural Network model for the prediction of the NO2 concentration [Details]
  • ESANN 2014 - On the complexity of shallow and deep neural network classifiers [Details]
Monica Bianchini
  • ESANN 2020 - Graph Neural Networks for the Prediction of Protein-Protein Interfaces [Details]
Monica Bianchini
  • ESANN 2021 - Complex Data: Learning Trustworthily, Automatically, and with Guarantees [Details]
Adrien Bibal
  • ESANN 2016 - Interpretability of machine learning models and representations: an introduction [Details]
  • ESANN 2018 - Finding the most interpretable MDS rotation for sparse linear models based on external features [Details]
Adrien Bibal
  • ESANN 2020 - Explaining t-SNE Embeddings Locally by Adapting LIME [Details]
Ioana Bica
  • ESANN 2018 - Multi-omics data integration using cross-modal neural networks [Details]
Michael Biehl
  • ESANN 2023 - Improved Interpretation of Feature Relevances: Iterated Relevance Matrix Analysis (IRMA) [Details]
  • ESANN 2023 - Layered Neural Networks with GELU Activation, a Statistical Mechanics Analysis [Details]
  • ESANN 2024 - Interpreting Hybrid AI through Autodecoded Latent Space Entities [Details]
  • ESANN 2024 - On-line Learning Dynamics in Layered Neural Networks with Arbitrary Activation Functions [Details]
Michael Biehl
  • ESANN 2005 - The dynamics of Learning Vector Quantization [Details]
  • ESANN 2006 - Classification of Boar Sperm Head Images using Learning Vector Quantization [Details]
  • ESANN 2007 - On the dynamics of Vector Quantization and Neural Gas [Details]
  • ESANN 2007 - Relevance matrices in LVQ [Details]
  • ESANN 2008 - Generalized matrix learning vector quantizer for the analysis of spectral data [Details]
  • ESANN 2008 - Phase transitions in Vector Quantization [Details]
  • ESANN 2009 - Adaptive Metrics for Content Based Image Retrieval in Dermatology [Details]
  • ESANN 2009 - Equilibrium properties of off-line LVQ [Details]
  • ESANN 2009 - Hyperparameter Learning in Robust Soft LVQ [Details]
  • ESANN 2009 - Nonlinear Discriminative Data Visualization [Details]
  • ESANN 2010 - Divergence based Learning Vector Quantization [Details]
  • ESANN 2010 - Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality Reduction and Visualization [Details]
  • ESANN 2011 - Causal relevance learning for robust classification under interventions [Details]
  • ESANN 2011 - Generalized functional relevance learning vector quantization [Details]
  • ESANN 2011 - Learning of causal relations [Details]
  • ESANN 2011 - Multivariate class labeling in Robust Soft LVQ [Details]
  • ESANN 2011 - Supervised dimension reduction mappings [Details]
  • ESANN 2012 - Adaptive learning for complex-valued data [Details]
  • ESANN 2012 - Matrix relevance LVQ in steroid metabolomics based classification of adrenal tumors [Details]
  • ESANN 2012 - Visualizing the quality of dimensionality reduction [Details]
  • ESANN 2013 - Non-Euclidean independent component analysis and Oja's learning [Details]
  • ESANN 2014 - Segmented shape-symbolic time series representation [Details]
  • ESANN 2015 - Combining dissimilarity measures for prototype-based classification [Details]
  • ESANN 2017 - Biomedical data analysis in translational research: integration of expert knowledge and interpretable models [Details]
  • ESANN 2017 - Comparison of strategies to learn from imbalanced classes for computer aided diagnosis of inborn steroidogenic disorders [Details]
  • ESANN 2018 - Machine learning and data analysis in astroinformatics [Details]
  • ESANN 2018 - Prototype-based analysis of GAMA galaxy catalogue data [Details]
  • ESANN 2019 - Feature relevance bounds for ordinal regression [Details]
  • ESANN 2019 - On-line learning dynamics of ReLU neural networks using statistical physics techniques [Details]
  • ESANN 2019 - Statistical physics of learning and inference [Details]
M. Biehl
  • ESANN 2002 - Supervised learning in committee machines by PCA [Details]