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Filippo Maria Bianchi
- ESANN 2020 - Pyramidal Graph Echo State Networks [Details]
- ESANN 2018 - Bidirectional deep-readout echo state networks [Details]
- ESANN 2018 - Learning compressed representations of blood samples time series with missing data [Details]
- ESANN 2021 - Deep learning for graphs [Details]
- ESANN 2023 - Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability [Details]
- ESANN 2013 - A One-Vs-One Classifier Ensemble With Majority Voting for Activity Recognition [Details]
- 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]
- ESANN 2021 - Complex Data: Learning Trustworthily, Automatically, and with Guarantees [Details]
- ESANN 2020 - Graph Neural Networks for the Prediction of Protein-Protein Interfaces [Details]
- ESANN 2020 - Explaining t-SNE Embeddings Locally by Adapting LIME [Details]
- 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]
- ESANN 2018 - Multi-omics data integration using cross-modal neural networks [Details]
- 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]
- ESANN 2025 - Interpretable machine learning for the diagnosis of hyperkinetic movement disorders [Details]
- ESANN 2025 - Mitigating the Bias in Data for Fairness Using an Advanced Generalized Learning Vector Quantization Approach -- FA(IR)$^2$MA-GLVQ [Details]
- ESANN 2025 - The Role of the Learning Rate in Layered Neural Networks with ReLU Activation Function [Details]
- 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]
- ESANN 2002 - Supervised learning in committee machines by PCA [Details]
- ESANN 2024 - Predicting the Closing Cross Auction Results at the NASDAQ Stock Exchange [Details]
- ESANN 2022 - Improving Intensive Care Chest X-Ray Classification by Transfer Learning and Automatic Label Generation [Details]
- ESANN 2023 - Segmentation and Analysis of Lumbar Spine MRI Scans for Vertebral Body Measurements [Details]
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- ESANN 2018 - Adaptive random forests for data stream regression [Details]
- ESANN 2019 - Recent trends in streaming data analysis, concept drift and analysis of dynamic data sets [Details]
- ESANN 2008 - Machine learning in cancer research: implications for personalised medicine [Details]
- ESANN 2021 - Complex Data: Learning Trustworthily, Automatically, and with Guarantees [Details]
- 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]
- ESANN 2019 - Detecting Black-box Adversarial Examples through Nonlinear Dimensionality Reduction [Details]
- ESANN 2019 - Societal Issues in Machine Learning: When Learning from Data is Not Enough [Details]
- ESANN 1993 - Probabilistic decision trees ans multilayered perceptrons [Details]
- ESANN 2004 - Three dimensional frames of reference transformations using gain modulated populations of neurons [Details]
- ESANN 1999 - A general approach to construct RBF net-based classifier [Details]
- ESANN 1999 - On the invertibility of the RBF model in a predictive control strategy [Details]
- ESANN 2017 - Detection of non-recurrent road traffic events based on clustering indicators [Details]
- ESANN 2025 - Performance monitoring and wear comprehension through Neural Network [Details]
- ESANN 2017 - Investigating optical transmission error correction using wavelet transforms [Details]
- ESANN 1998 - Lazy learning for control design [Details]
- ESANN 2000 - A multi-steap ahead prediction method based on local dynamic properties [Details]
- ESANN 2025 - Efficient Training of Neural SDEs Using Stochastic Optimal Control [Details]
- ESANN 2009 - Multi-task Preference learning with Gaussian Processes [Details]
- ESANN 2018 - Active Learning based on Transfer Learning Techniques for Image Classification [Details]
- ESANN 2000 - An optimization neural network model with time-dependent and lossy dynamics [Details]