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ORCIDNikolaos Gianniotis
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2010 – 2019
- 2019
[c12]Nikolaos Gianniotis
:
Mixed Variational Inference. IJCNN 2019: 1-8- [i4]Nikolaos Gianniotis:
Mixed Variational Inference. CoRR abs/1901.04791 (2019) - [i3]Nikolaos Gianniotis, Christoph Schnörr, Christian Molkenthin, Sanjay Singh Bora:
Approximate Variational Inference Based on a Finite Sample of Gaussian Latent Variables. CoRR abs/1906.04507 (2019) - 2017
- [c11]Nikolaos Gianniotis:
Linear Dimensionality Reduction for Time Series. ICONIP (1) 2017: 375-383 - 2016
- [j4]Nikolaos Gianniotis, Sven Dennis Kügler, Peter Tiño, Kai Lars Polsterer:
Model-coupled autoencoder for time series visualisation. Neurocomputing 192: 139-146 (2016) - [j3]Nikolaos Gianniotis, Christoph Schnörr, Christian Molkenthin, Sanjay Singh Bora:
Approximate variational inference based on a finite sample of Gaussian latent variables. Pattern Anal. Appl. 19(2): 475-485 (2016) - [c10]Kai Lars Polsterer, Fabian Gieseke, Christian Igel, Bernd Doser, Nikolaos Gianniotis:
Parallelized rotation and flipping INvariant Kohonen maps (PINK) on GPUs. ESANN 2016 - [c9]Sven Dennis Kügler, Nikolaos Gianniotis, Kai Lars Polsterer:
A spectral model for multimodal redshift estimation. SSCI 2016: 1-8 - [i2]Nikolaos Gianniotis, Sven Dennis Kügler, Peter Tiño, Kai Lars Polsterer:
Model-Coupled Autoencoder for Time Series Visualisation. CoRR abs/1601.05654 (2016) - 2015
- [c8]Nikolaos Gianniotis, Sven Dennis Kügler, Peter Tiño, Kai Polsterer, Ranjeev Misra:
Autoencoding time series for visualisation. ESANN 2015 - [i1]Nikolaos Gianniotis, Sven Dennis Kügler, Peter Tiño, Kai Polsterer, Ranjeev Misra:
Autoencoding Time Series for Visualisation. CoRR abs/1505.00936 (2015) - 2014
- [j2]Nikolaos Gianniotis, Nicolas Kuehn, Frank Scherbaum:
Manifold aligned ground motion prediction equations for regional datasets. Comput. Geosci. 69: 72-77 (2014) - 2013
- [c7]Nikolaos Gianniotis:
Interpretable magnification factors for topographic maps of high dimensional and structured data. CIDM 2013: 238-245 - [c6]Nikolaos Gianniotis, Carsten Riggelsen:
Visualisation of high-dimensional data using an ensemble of neural networks. CIEL 2013: 17-24 - 2012
- [c5]Nikolaos Gianniotis, Carsten Riggelsen, Nicolas Kühn, Frank Scherbaum:
Autoencoding Ground Motion Data for Visualisation. ICANN (2) 2012: 395-402
2000 – 2009
- 2009
- [c4]Nikolaos Gianniotis, Peter Tiño:
Visualization of Structured Data via Generative Probabilistic Modeling. Similarity-Based Clustering 2009: 118-137 - [c3]Nikolaos Gianniotis, Peter Tiño, Steve Spreckley, Somak Raychaudhury:
Topographic Mapping of Astronomical Light Curves via a Physically Inspired Probabilistic Model. ICANN (1) 2009: 567-576 - 2008
- [b1]Nikolaos Gianniotis:
Visualisation of structured data through generative probabilistic modeling. University of Birmingham, UK, 2008 - [j1]Nikolaos Gianniotis, Peter Tiño:
Visualization of Tree-Structured Data Through Generative Topographic Mapping. IEEE Trans. Neural Networks 19(8): 1468-1493 (2008) - 2007
- [c2]Nikolaos Gianniotis, Peter Tiño:
Visualisation of tree-structured data through generative probabilistic modelling. ESANN 2007: 97-102 - [c1]Peter Tiño, Nikolaos Gianniotis:
Metric Properties of Structured Data Visualizations through Generative Probabilistic Modeling. IJCAI 2007: 1083-1088
Coauthor Index
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