| Year |
Citation |
Score |
| 2015 |
Klampfl S, Kern R. Machine learning techniques for automatically extracting contextual Information from scientific publications Communications in Computer and Information Science. 548: 105-116. DOI: 10.1007/978-3-319-25518-7_9 |
1 |
|
| 2015 |
Rexha A, Klampfl S, Kröll M, Kern R. Towards authorship attribution for bibliometrics using stylometric features Ceur Workshop Proceedings. 1384: 44-49. |
1 |
|
| 2014 |
Horn C, Klampfl S, Cik M, Reiter T. Detecting outliers in cell phone data Transportation Research Record. 49-56. DOI: 10.3141/2405-07 |
1 |
|
| 2014 |
Kröll M, Klampfl S, Kern R. Towards a marketplace for the scientific community: Accessing knowledge from the computer science domain D-Lib Magazine. 20. DOI: 10.1045/november14-kroell |
1 |
|
| 2014 |
Klampfl S, Jack K, Kern R. A comparison of two unsupervised table recognition methods from digital scientific articles D-Lib Magazine. 20. DOI: 10.1045/november14-klampfl |
1 |
|
| 2014 |
Klampfl S, Granitzer M, Jack K, Kern R. Unsupervised document structure analysis of digital scientific articles International Journal On Digital Libraries. 14: 83-99. DOI: 10.1007/s00799-014-0115-1 |
1 |
|
| 2013 |
Klampfl S, Maass W. Emergence of dynamic memory traces in cortical microcircuit models through STDP. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 33: 11515-29. PMID 23843522 DOI: 10.1523/JNEUROSCI.5044-12.2013 |
1 |
|
| 2013 |
Kern R, Klampfl S. Extraction of references using layout and formatting information from scientific articles D-Lib Magazine. 19. DOI: 10.1045/september2013-kern |
1 |
|
| 2013 |
Klampfl S, Kern R. An unsupervised machine learning approach to body text and table of contents extraction from digital scientific articles Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8092: 144-155. DOI: 10.1007/978-3-642-40501-3_15 |
1 |
|
| 2012 |
Klampfl S, David SV, Yin P, Shamma SA, Maass W. A quantitative analysis of information about past and present stimuli encoded by spikes of A1 neurons. Journal of Neurophysiology. 108: 1366-80. PMID 22696538 DOI: 10.1152/jn.00935.2011 |
1 |
|
| 2010 |
Klampfl S, Maass W. A theoretical basis for emergent pattern discrimination in neural systems through slow feature extraction. Neural Computation. 22: 2979-3035. PMID 20858129 DOI: 10.1162/NECO_a_00050 |
1 |
|
| 2009 |
Klampfl S, Legenstein R, Maass W. Spiking neurons can learn to solve information bottleneck problems and extract independent components. Neural Computation. 21: 911-59. PMID 19018708 DOI: 10.1162/neco.2008.01-07-432 |
1 |
|
| 2009 |
Klampfl S, Maass W. Replacing supervised classification learning by slow feature analysis in spiking neural networks Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference. 988-996. |
1 |
|
| 2007 |
Klampfl S, Legenstein R, Maass W. Information bottleneck optimization and independent component extraction with spiking neurons Advances in Neural Information Processing Systems. 713-720. |
1 |
|
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