14 Publikationen
2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2957385
Risse N, Göpfert C, Göpfert JP. How to Compare Adversarial Robustness of Classifiers from a Global Perspective. In: Farkaš I, Masulli P, Otte S, Wermter S, eds. Artificial Neural Networks and Machine Learning – ICANN 2021. 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part I. Lecture Notes in Computer Science. Vol 12891. Cham: Springer International Publishing; 2021: 29-41.
PUB | DOI
2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2935456

Pfannschmidt L, Göpfert C, Neumann U, Heider D, Hammer B. FRI - Feature Relevance Intervals for Interpretable and Interactive Data Exploration. Presented at the 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, Certosa di Pontignano, Siena - Tuscany, Italy.
PUB
| PDF | DOI | arXiv
2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2932412
Straat M, Abadi F, Göpfert C, Hammer B, Biehl M. Statistical Mechanics of On-Line Learning Under Concept Drift. ENTROPY. 2018;20(10): 775.
PUB | DOI | WoS | PubMed | Europe PMC
2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2911900
Paaßen B, Göpfert C, Hammer B. Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces. Neural Processing Letters. 2018;48(2):669-689.
PUB | DOI | Download (ext.) | WoS | arXiv
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2908201

Göpfert C, Pfannschmidt L, Hammer B. Feature Relevance Bounds for Linear Classification. In: Verleysen M, ed. Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve: Ciaco - i6doc.com; 2017: 187--192.
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| Dateien verfügbar | Download (ext.)
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2913752

Göpfert JP, Göpfert C, Botsch M, Hammer B. Effects of Variability in Synthetic Training Data on Convolutional Neural Networks for 3D Head Reconstruction. In: 2017 SSCI Proceedings. 2017 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, NJ: IEEE; 2017.
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| PDF | DOI
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2915274

Göpfert C, Göpfert JP, Hammer B. Analyzing Feature Relevance for Linear Reject Option SVM using Relevance Intervals. In: Proceedings of the 2017 NIPS workshop on Transparent and Interpretable Machine Learning in Safety Critical Environments. 2017.
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| PDF
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909367
Kummert J, Paaßen B, Jensen J, Göpfert C, Hammer B. Local Reject Option for Deterministic Multi-class SVM. In: E.P. Villa A, Masulli P, Pons Rivero AJ, eds. Artificial Neural Networks and Machine Learning - ICANN 2016 - 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. Lecture Notes in Computer Science. Vol 9887. Cham: Springer Nature; 2016: 251--258.
PUB | DOI
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900676

Paaßen B, Göpfert C, Hammer B. Gaussian process prediction for time series of structured data. In: Verleysen M, ed. Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve: Ciaco - i6doc.com; 2016: 41--46.
PUB
| PDF
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905729

Göpfert C, Paaßen B, Hammer B. Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning. In: E.P. Villa A, Masulli P, Pons Rivero AJ, eds. Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. Lecture Notes in Computer Science. Vol 9887. Cham: Springer Nature; 2016: 510-517.
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14 Publikationen
2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2957385
Risse N, Göpfert C, Göpfert JP. How to Compare Adversarial Robustness of Classifiers from a Global Perspective. In: Farkaš I, Masulli P, Otte S, Wermter S, eds. Artificial Neural Networks and Machine Learning – ICANN 2021. 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part I. Lecture Notes in Computer Science. Vol 12891. Cham: Springer International Publishing; 2021: 29-41.
PUB | DOI
2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2935456

Pfannschmidt L, Göpfert C, Neumann U, Heider D, Hammer B. FRI - Feature Relevance Intervals for Interpretable and Interactive Data Exploration. Presented at the 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, Certosa di Pontignano, Siena - Tuscany, Italy.
PUB
| PDF | DOI | arXiv
2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2932412
Straat M, Abadi F, Göpfert C, Hammer B, Biehl M. Statistical Mechanics of On-Line Learning Under Concept Drift. ENTROPY. 2018;20(10): 775.
PUB | DOI | WoS | PubMed | Europe PMC
2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2911900
Paaßen B, Göpfert C, Hammer B. Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces. Neural Processing Letters. 2018;48(2):669-689.
PUB | DOI | Download (ext.) | WoS | arXiv
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2908201

Göpfert C, Pfannschmidt L, Hammer B. Feature Relevance Bounds for Linear Classification. In: Verleysen M, ed. Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve: Ciaco - i6doc.com; 2017: 187--192.
PUB
| Dateien verfügbar | Download (ext.)
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2913752

Göpfert JP, Göpfert C, Botsch M, Hammer B. Effects of Variability in Synthetic Training Data on Convolutional Neural Networks for 3D Head Reconstruction. In: 2017 SSCI Proceedings. 2017 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, NJ: IEEE; 2017.
PUB
| PDF | DOI
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2915274

Göpfert C, Göpfert JP, Hammer B. Analyzing Feature Relevance for Linear Reject Option SVM using Relevance Intervals. In: Proceedings of the 2017 NIPS workshop on Transparent and Interpretable Machine Learning in Safety Critical Environments. 2017.
PUB
| PDF
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909367
Kummert J, Paaßen B, Jensen J, Göpfert C, Hammer B. Local Reject Option for Deterministic Multi-class SVM. In: E.P. Villa A, Masulli P, Pons Rivero AJ, eds. Artificial Neural Networks and Machine Learning - ICANN 2016 - 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. Lecture Notes in Computer Science. Vol 9887. Cham: Springer Nature; 2016: 251--258.
PUB | DOI
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900676

Paaßen B, Göpfert C, Hammer B. Gaussian process prediction for time series of structured data. In: Verleysen M, ed. Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve: Ciaco - i6doc.com; 2016: 41--46.
PUB
| PDF
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905729

Göpfert C, Paaßen B, Hammer B. Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning. In: E.P. Villa A, Masulli P, Pons Rivero AJ, eds. Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. Lecture Notes in Computer Science. Vol 9887. Cham: Springer Nature; 2016: 510-517.
PUB
| PDF | DOI