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R. Dogaru
- ESANN 1996 - Fast signal recognition and detection using ART1 neural networks and nonlinear preprocessing units based on time delay embeddings [Details]
- ESANN 2020 - Improving Light-weight Convolutional Neural Networks for Face Recognition Targeting Resource Constrained Platforms [Details]
- ESANN 2019 - Deep RL for autonomous robots: limitations and safety challenges [Details]
- ESANN 2004 - Non-Euclidean norms and data normalisation [Details]
- ESANN 2005 - TreeGNG - hierarchical topological clustering [Details]
- ESANN 2006 - Topological Correlation [Details]
- ESANN 2015 - PCA-based algorithm for feature score measures ensemble construction [Details]
- ESANN 2005 - An artificial neural network for analysing the survival of patients with colorectal cancer [Details]
- ESANN 2014 - Utilization of Chemical Structure Information for Analysis of Spectra Composites [Details]
- ESANN 2015 - Learning matrix quantization and variants of relevance learning [Details]
- ESANN 2018 - Interpreting deep learning models for ordinal problems [Details]
- ESANN 2017 - Latent variable analysis in hospital electric power demand using non-negative matrix factorization [Details]
- ESANN 2010 - An ART-type network approach for video object detection [Details]
- ESANN 2010 - Web Document Clustering based on a Hierarchical Self-Organizing Model [Details]
- ESANN 2003 - Neural Network Algorithms for the p-Median Problem [Details]
- ESANN 2001 - CMOS design of focal plane programmable array processors [Details]
- ESANN 2002 - Double self-organizing maps to cluster gene expression data [Details]
- ESANN 2002 - Improving robustness of fuzzy gene modeling [Details]
- ESANN 2007 - A hierarchical model for syllable recognition [Details]
- ESANN 1999 - Extraction of intrinsic dimension using CCA - Application to blind sources separation [Details]
- ESANN 2000 - A robust non-linear projection method [Details]
- ESANN 2004 - HMM and IOHMM modeling of EEG rhythms for asynchronous BCI systems [Details]
- ESANN 2002 - Connectionist models investigating representations formed in the sequential generation of characters [Details]
- ESANN 2017 - Learning sparse models of diffusive graph signals [Details]
- ESANN 2019 - Preconditioned conjugate gradient algorithms for graph regularized matrix completion [Details]
- ESANN 2020 - Learning Deep Fair Graph Neural Networks [Details]
- ESANN 2014 - Easy multiple kernel learning [Details]
- ESANN 2015 - Feature and kernel learning [Details]
- ESANN 2016 - Advances in Learning with Kernels: Theory and Practice in a World of growing Constraints [Details]
- ESANN 2016 - Measuring the Expressivity of Graph Kernels through the Rademacher Complexity [Details]
- ESANN 2017 - Fast hyperparameter selection for graph kernels via subsampling and multiple kernel learning [Details]
- ESANN 2017 - Learning dot-product polynomials for multiclass problems [Details]
- ESANN 2018 - Emerging trends in machine learning: beyond conventional methods and data [Details]
- ESANN 2019 - PAC-Bayes and Fairness: Risk and Fairness Bounds on Distribution Dependent Fair Priors [Details]
- ESANN 2018 - Fast Power system security analysis with Guided Dropout [Details]
- ESANN 2019 - LEAP nets for power grid perturbations [Details]
- ESANN 2002 - When does geodesic distance recover the true hidden parametrization of families of articulated images? [Details]
- ESANN 2019 - LEAP nets for power grid perturbations [Details]
- ESANN 2011 - Mutual information based feature selection for mixed data [Details]
- ESANN 2011 - Mutual information for feature selection with missing data [Details]
- ESANN 2012 - On the Potential Inadequacy of Mutual Information for Feature Selection [Details]
- ESANN 2013 - Risk Estimation and Feature Selection [Details]
- ESANN 1993 - MLP modular networks for multi-class recognition [Details]
- ESANN 2003 - Statistical downscaling with artificial neural networks [Details]
- ESANN 2002 - Heteroscedastic regularised kernel regression for prediction of episodes of poor air quality [Details]
- ESANN 2003 - Approximately unbiased estimation of conditional variance in heteroscedastic kernel ridge regression [Details]
- ESANN 2010 - Finding correlations in multimodal data using decomposition approaches [Details]
- ESANN 2020 - Visualization of the Feature Space of Neural Networks [Details]