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H. Speckmann
- ESANN 1994 - Improvement of learning results of the selforganizing map by calculating fractal dimensions [Details]
- ESANN 2021 - Complex Data: Learning Trustworthily, Automatically, and with Guarantees [Details]
- ESANN 2021 - Tangent Graph Convolutional Network [Details]
- ESANN 2022 - Biased Edge Dropout in NIFTY for Fair Graph Representation Learning [Details]
- ESANN 2023 - An Empirical Study of Over-Parameterized Neural Models based on Graph Random Features [Details]
- ESANN 2023 - Real-time Detection of Evoked Potentials by Deep Learning: a Case Study [Details]
- ESANN 2024 - Towards the application of Backpropagation-Free Graph Convolutional Networks on Huge Datasets [Details]
- ESANN 2020 - A Systematic Assessment of Deep Learning Models for Molecule Generation [Details]
- ESANN 2020 - Deep Recurrent Graph Neural Networks [Details]
- ESANN 2020 - Linear Graph Convolutional Networks [Details]
- ESANN 2002 - A general framework for unsupervised processing of structured data [Details]
- ESANN 2004 - a preliminary experimental comparison of recursive neural networks and a tree kernel method for QSAR/QSPR regression tasks [Details]
- ESANN 2005 - Contextual Processing of Graphs using Self-Organizing Maps [Details]
- ESANN 2006 - Unsupervised clustering of continuous trajectories of kinematic trees with SOM-SD [Details]
- ESANN 2007 - "Kernelized" Self-Organizing Maps for Structured Data [Details]
- ESANN 2008 - Self-Organizing Maps for cyclic and unbounded graphs [Details]
- ESANN 2009 - Projection of undirected and non-positional graphs using Self Organizing Maps [Details]
- ESANN 2009 - Supervised learning as preference optimization [Details]
- ESANN 2010 - Heuristics Miner for Time Intervals [Details]
- ESANN 2011 - Sparsity Issues in Self-Organizing-Maps for Structures [Details]
- ESANN 2012 - Assessment of sequential Boltmann machines on a lexical processing task [Details]
- ESANN 2012 - Input-Output Hidden Markov Models for trees [Details]
- ESANN 2014 - A HMM-based pre-training approach for sequential data [Details]
- ESANN 2015 - Exploiting the ODD framework to define a novel effective graph kernel [Details]
- ESANN 2016 - Challenges in Deep Learning [Details]
- ESANN 2016 - Measuring the Expressivity of Graph Kernels through the Rademacher Complexity [Details]
- ESANN 2017 - Approximated Neighbours MinHash Graph Node Kernel [Details]
- ESANN 2017 - The Conjunctive Disjunctive Node Kernel [Details]
- ESANN 2018 - DEEP: decomposition feature enhancement procedure for graphs [Details]
- ESANN 2019 - Embeddings and Representation Learning for Structured Data [Details]
- ESANN 2019 - On the definition of complex structured feature spaces [Details]
- ESANN 2014 - Exploiting similarity in system identification tasks with recurrent neural networks [Details]
- ESANN 2017 - Attention-based Information Fusion using Multi-Encoder-Decoder Recurrent Neural Networks [Details]
- ESANN 2024 - Safety-Oriented Pruning and Interpretation of Reinforcement Learning Policies [Details]
- ESANN 2023 - Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability [Details]
- ESANN 2023 - A Protocol for Continual Explanation of SHAP [Details]
- ESANN 2024 - Enhancing Echo State Networks with Gradient-based Explainability Methods [Details]
- ESANN 2008 - Multilayer perceptron to model the decarburization process in stainless steel production [Details]
- ESANN 2003 - Developmental pruning of synapses and category learning [Details]
- ESANN 2010 - Modelling the McGurk effect [Details]
- ESANN 1998 - A self-organising neural network for modelling cortical development [Details]
- ESANN 1998 - Learning sensory-motor cortical mappings without training [Details]
- ESANN 2004 - Modelling of biologically plausible excitatory networks: emergence and modulation of neural synchrony [Details]
- ESANN 2004 - Learning by geometrical shape changes of dendritic spines [Details]
- ESANN 2018 - Image-to-Text Transduction with Spatial Self-Attention [Details]
- ESANN 2018 - Continuous convolutional object tracking [Details]
- No papers found
- ESANN 2022 - Improving Intensive Care Chest X-Ray Classification by Transfer Learning and Automatic Label Generation [Details]
- ESANN 2009 - Generalisation of action sequences in RNNPB networks with mirror properties [Details]
- ESANN 2012 - One Class SVM and Canonical Correlation Analysis increase performance in a c-VEP based Brain-Computer Interface (BCI) [Details]
- ESANN 2013 - Decoding stimulation intensity from evoked ECoG activity using support vector regression [Details]
- ESANN 2020 - Fast and Stable Interval Bounds Propagation for Training Verifiably Robust Models [Details]
- ESANN 2012 - Highly efficient localisation utilising weightless neural systems [Details]
- ESANN 2019 - Multi-target feature selection through output space clustering [Details]
- ESANN 2012 - Discriminant functional gene groups identification with machine learning and prior knowledge [Details]
- ESANN 2005 - Generalized Relevance LVQ with Correlation Measures for Biological Data [Details]
- ESANN 2006 - Sanger-driven MDSLocalize - a comparative study for genomic data [Details]
- ESANN 2020 - An Empirical Study of Iterative Knowledge Distillation for Neural Network Compression [Details]
- ESANN 2023 - Exploring Strategies for Modeling Sign Language Phonology [Details]
- ESANN 2001 - Bayesian decision theory on three layered neural networks [Details]