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Barbara Hammer
- ESANN 2004 - Neural methods for non-standard data [Details]
- ESANN 2020 - Efficient computation of counterfactual explanations of LVQ models [Details]
- ESANN 2020 - Locally Adaptive Nearest Neighbors [Details]
- ESANN 2020 - Sparse Metric Learning in Prototype-based Classification [Details]
- ESANN 2021 - Application of Graph Convolutions in a Lightweight Model for Skeletal Human Motion Forecasting [Details]
- ESANN 2021 - Federated Learning Vector Quantization [Details]
- ESANN 2021 - Machine Learning for Measuring and Analyzing Online Social Communications [Details]
- ESANN 2022 - Federated learning vector quantization for dealing with drift between nodes [Details]
- ESANN 2022 - From hyperspectral to multispectral sensing – from simulation to reality: A comprehensive approach for calibration model transfer [Details]
- ESANN 2022 - Improving Zorro Explanations for Sparse Observations with Dense Proxy Data [Details]
- ESANN 2022 - Model Agnostic Local Explanations of Reject [Details]
- ESANN 2022 - Neural Architecture Search for Sentence Classification with BERT [Details]
- ESANN 2023 - On Feature Removal for Explainability in Dynamic Environments [Details]
- ESANN 2023 - Robust Feature Selection and Robust Training to Cope with Hyperspectral Sensor Shifts [Details]
- ESANN 2024 - Causes of Rejects in Prototype-based Classification Aleatoric vs. Epistemic Uncertainty [Details]
- ESANN 2024 - Machine learning in distributed, federated and non-stationary environments - recent trends [Details]
- ESANN 2024 - Noise Robust One-Class Intrusion Detection on Dynamic Graphs [Details]
- ESANN 2024 - Self-Supervised Learning from Incrementally Drifting Data Streams [Details]
- ESANN 2024 - Similarity-Based Zero-Shot Domain Adaptation for Wearables [Details]
- ESANN 2024 - Trust in Artificial Intelligence: Beyond Interpretability [Details]
- ESANN 2024 - Visualizing and Improving 3D Mesh Segmentation with DeepView [Details]
- ESANN 2025 - Compression-based $k$NN for Class Incremental Continual Learning [Details]
- ESANN 2025 - Conceptualizing Concept Drift [Details]
- ESANN 2025 - Don't drift away: Advances and Applications of Streaming and Continual Learning [Details]
- ESANN 2025 - Evaluating Concept Discovery Methods for Sensitive Attributes in Language Models [Details]
- ESANN 2025 - Explaining Outliers using Isolation Forest and Shapley Interactions [Details]
- ESANN 2025 - JEPA for RL: Investigating Joint-Embedding Predictive Architectures for Reinforcement Learning [Details]
- ESANN 2025 - Solving Turbulent Rayleigh-Bénard Convection using Fourier Neural Operators [Details]
- ESANN 1998 - Training a sigmoidal network is difficult [Details]
- ESANN 1999 - Approximation capabilities of folding networks [Details]
- ESANN 2000 - Limitations of hybrid systems [Details]
- ESANN 2001 - Input pruning for neural gas architectures [Details]
- ESANN 2001 - Relevance determination in Learning Vector Quantization [Details]
- ESANN 2002 - A general framework for unsupervised processing of structured data [Details]
- ESANN 2002 - Batch-RLVQ [Details]
- ESANN 2002 - Perspectives on learning with recurrent neural networks [Details]
- ESANN 2003 - Improving iterative repair strategies for scheduling with the SVM [Details]
- ESANN 2003 - Mathematical Aspects of Neural Networks [Details]
- ESANN 2003 - Monitoring technical systems with prototype based clustering [Details]
- ESANN 2003 - Unsupervised Recursive Sequence Processing [Details]
- ESANN 2004 - self-organizing context learning [Details]
- ESANN 2005 - Classification using non-standard metrics [Details]
- ESANN 2005 - Relevance determination in reinforcement learning [Details]
- ESANN 2005 - Relevance learning for mental disease classification [Details]
- ESANN 2005 - The dynamics of Learning Vector Quantization [Details]
- ESANN 2006 - Magnification control for batch neural gas [Details]
- ESANN 2006 - Margin based Active Learning for LVQ Networks [Details]
- ESANN 2006 - Neural networks and machine learning in bioinformatics - theory and applications [Details]
- ESANN 2007 - How to process uncertainty in machine learning? [Details]
- ESANN 2007 - On the dynamics of Vector Quantization and Neural Gas [Details]
- ESANN 2007 - Relevance matrices in LVQ [Details]
- ESANN 2008 - Magnification Control in Relational Neural Gas [Details]
- ESANN 2008 - Parallelizing single patch pass clustering [Details]
- ESANN 2009 - Equilibrium properties of off-line LVQ [Details]
- ESANN 2009 - Hyperparameter Learning in Robust Soft LVQ [Details]
- ESANN 2009 - Median Variant of Fuzzy c-Means [Details]
- ESANN 2009 - Nonlinear Discriminative Data Visualization [Details]
- ESANN 2009 - Recent advances in efficient learning of recurrent networks [Details]
- ESANN 2010 - Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality Reduction and Visualization [Details]
- ESANN 2010 - Relational Generative Topographic Map [Details]
- ESANN 2010 - Relevance learning in generative topographic maps [Details]
- ESANN 2010 - Sparse representation of data [Details]
- ESANN 2011 - Generalized functional relevance learning vector quantization [Details]
- ESANN 2011 - Patch Affinity Propagation [Details]
- ESANN 2011 - Supervised dimension reduction mappings [Details]
- ESANN 2012 - Out-of-sample kernel extensions for nonparametric dimensionality reduction [Details]
- ESANN 2012 - Recent developments in clustering algorithms [Details]
- ESANN 2012 - Relevance learning for time series inspection [Details]
- ESANN 2012 - Visualizing the quality of dimensionality reduction [Details]
- ESANN 2013 - Semi-Supervised Vector Quantization for proximity data [Details]
- ESANN 2013 - Sparse approximations for kernel learning vector quantization [Details]
- ESANN 2013 - Visualizing dependencies of spectral features using mutual information [Details]
- ESANN 2014 - Adaptive distance measures for sequential data [Details]
- ESANN 2014 - Learning and modeling big data [Details]
- ESANN 2014 - Rejection strategies for learning vector quantization [Details]
- ESANN 2014 - Relevance Learning for Dimensionality Reduction [Details]
- ESANN 2014 - Supervised Generative Models for Learning Dissimilarity Data [Details]
- ESANN 2015 - Adaptive structure metrics for automated feedback provision in Java programming [Details]
- ESANN 2015 - Certainty-based prototype insertion/deletion for classification with metric adaptation [Details]
- ESANN 2015 - Unsupervised Dimensionality Reduction for Transfer Learning [Details]
- ESANN 2016 - Choosing the best algorithm for an incremental on-line learning task [Details]
- ESANN 2016 - Discriminative dimensionality reduction in kernel space [Details]
- ESANN 2016 - Gaussian process prediction for time series of structured data [Details]
- ESANN 2016 - Incremental learning algorithms and applications [Details]
- ESANN 2017 - An EM transfer learning algorithm with applications in bionic hand prostheses [Details]
- ESANN 2017 - Feature Relevance Bounds for Linear Classification [Details]
- ESANN 2018 - Differential private relevance learning [Details]
- ESANN 2018 - Feasibility based Large Margin Nearest Neighbor metric learning [Details]
- ESANN 2019 - Feature relevance bounds for ordinal regression [Details]
- ESANN 2019 - Multiple-Kernel dictionary learning for reconstruction and clustering of unseen multivariate time-series [Details]
- ESANN 2019 - Recent trends in streaming data analysis, concept drift and analysis of dynamic data sets [Details]
- ESANN 2019 - time series modelling of market price in real-time bidding [Details]
- ESANN 2006 - Probabilistic classifiers and time-scale representations: application to the monitoring of a tramway guiding system [Details]
- ESANN 2002 - Forecasting using twinned principal curves [Details]
- ESANN 2006 - Random Forests Feature Selection with K-PLS: Detecting Ischemia from Magnetocardiograms [Details]
- ESANN 2012 - Automatic Group-Outlier Detection [Details]
- ESANN 2019 - Deep Embedded SOM: joint representation learning and self-organization [Details]
- No papers found
- ESANN 2022 - Battery detection of XRay images using transfer learning [Details]
- ESANN 2023 - Don’t waste SAM [Details]
- ESANN 2004 - Evolutionary Optimization of Neural Networks for Face Detection [Details]
- ESANN 2015 - A simple technique for improving multi-class classification with neural networks [Details]
- ESANN 2016 - Comparison of Four- and Six-Layered Configurations for Deep Network Pretraining [Details]
- ESANN 2008 - Safe exploration for reinforcement learning [Details]
- ESANN 2010 - The Markov Decision Process Extraction Network [Details]
- ESANN 2011 - Ensemble Usage for More Reliable Policy Identification in Reinforcement Learning [Details]
- ESANN 2012 - Recurrent Neural State Estimation in Domains with Long-Term Dependencies [Details]
- ESANN 2015 - Fast greedy insertion and deletion in sparse Gaussian process regression [Details]
- No papers found
- ESANN 2025 - Deciphering Barlow Twins: Reduncy Reduction is Insufficient and Normalization is Key [Details]
- ESANN 2015 - Robust Visual Terrain Classification with Recurrent Neural Networks [Details]
- ESANN 2005 - Mutual information and gamma test for input selection [Details]
- ESANN 2006 - Determination of the Mahalanobis matrix using nonparametric noise estimations [Details]
- No papers found
- No papers found
- ESANN 2007 - A metamorphosis of Canonical Correlation Analysis into multivariate maximum margin learning [Details]
- ESANN 2010 - An augmented efficient backpropagation training strategy for deep autoassociative neural networks [Details]
- ESANN 2023 - Knowledge Distillation for Anomaly Detection [Details]
- ESANN 2018 - Evolutionary Composition of Customized Fault Localization Heuristics [Details]
- ESANN 1999 - A kernel based adaline [Details]
- ESANN 1999 - An efficient formulation of sparsity controlled support vector regression [Details]
- ESANN 2025 - Evolutionary Fault Localization Based on the Diversity of Suspiciousness Values [Details]
- ESANN 2012 - Towards biologically realistic multi-compartment neuron model emulation in analog VLSI [Details]
- ESANN 2020 - SDOstream: Low-Density Models for Streaming Outlier Detection [Details]
- No papers found