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Martin Meier
- ESANN 2013 - Perceptual grouping through competition in coupled oscillator networks [Details]
- ESANN 1997 - Size invariance by dynamic scaling in neural vision systems [Details]
- ESANN 1998 - Polyhedral mixture of linear experts for many-to-one mapping inversion [Details]
- ESANN 2023 - Automated green machine learning for condition-based maintenance [Details]
- ESANN 2017 - The Top 10 Topics in Machine Learning Revisited: A Quantitative Meta-Study [Details]
- ESANN 2018 - Combining latent tree modeling with a random forest-based approach, for genetic association studies [Details]
- ESANN 2016 - Learning with hard constraints as a limit case of learning with soft constraints [Details]
- ESANN 2012 - An analysis of Gaussian-binary restricted Boltzmann machines for natural images [Details]
- ESANN 2020 - Anomaly Detection Approach in Cyber Security for User and Entity Behavior Analytics System [Details]
- ESANN 2015 - Real-time activity recognition via deep learning of motion features [Details]
- ESANN 2016 - Deep Learning Vector Quantization [Details]
- ESANN 2002 - Neural dimensionality reduction for document processing [Details]
- ESANN 2015 - A Robust Neural Robot Navigation Using a Combination of Deliberative and Reactive Control Architectures [Details]
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- ESANN 2022 - Pruning Weightless Neural Networks [Details]
- ESANN 2024 - Interactive Machine Learning-Powered Dashboard for Energy Analytics in Residential Buildings [Details]
- ESANN 2024 - Learning Kernel Parameters for Support Vector Classification Using Similarity Embeddings [Details]
- ESANN 2005 - Domain expert approximation through oracle learning [Details]
- ESANN 2019 - Design of Power-Efficient FPGA Convolutional Cores with Approximate Log Multiplier [Details]
- ESANN 2014 - A new approach for multiple instance learning based on a homogeneity bag operator [Details]
- ESANN 2019 - Transfer Learning for transferring machine-learning based models among hyperspectral sensors [Details]
- ESANN 2021 - Investigating Intensity and Transversal Drift in Hyperspectral Imaging Data [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 2010 - Finding correlations in multimodal data using decomposition approaches [Details]
- ESANN 2008 - Classification of chestnuts with feature selection by noise resilient classifiers [Details]
- ESANN 2023 - Hybrid modelling of dynamic anaerobic digestion process in full-scale with LSTM NN and BMP measurements [Details]
- ESANN 2021 - Robust Malware Classification via Deep Graph Networks on Call Graph Topologies [Details]
- ESANN 2014 - A robust regularization path for the Doubly Regularized Support Vector Machine [Details]
- ESANN 2019 - training networks separately on static and dynamic obstacles improves collision avoidance during indoor robot navigation [Details]
- ESANN 1994 - A comparison of neural networks, linear controllers, genetic algorithms and simulated annealing for real time control [Details]
- ESANN 2004 - Forbidden Magnification? I. [Details]
- ESANN 2006 - Data topology visualization for the Self-Organizing Map [Details]
- ESANN 2006 - Weighted differential topographic function: a refinement of topographic function [Details]
- ESANN 2008 - Machine learning approches and pattern recognition for spectral data [Details]
- ESANN 2012 - Parallel neural hardware: the time is right [Details]
- ESANN 2012 - Unmixing Hyperspectral Images with Fuzzy Supervised Self-Organizing Maps [Details]
- ESANN 2012 - gNBXe -- a Reconfigurable Neuroprocessor for Various Types of Self-Organizing Maps [Details]
- ESANN 2004 - Forbidden magnification? II. [Details]