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ORCIDNiels Landwehr
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- affiliation: University of Potsdam, Germany
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2020 – today
- 2024
[c21]Ahmad Bdeir, Kristian Schwethelm
, Niels Landwehr:
Fully Hyperbolic Convolutional Neural Networks for Computer Vision. ICLR 2024- [i9]Ahmad Bdeir, Niels Landwehr:
Optimizing Curvature Learning for Robust Hyperbolic Deep Learning in Computer Vision. CoRR abs/2405.13979 (2024) - 2023
- [j19]Saddam Hijazi, Melina A. Freitag, Niels Landwehr:
POD-Galerkin reduced order models and physics-informed neural networks for solving inverse problems for the Navier-Stokes equations. Adv. Model. Simul. Eng. Sci. 10(1): 5 (2023) - [i8]Ahmad Bdeir, Kristian Schwethelm, Niels Landwehr:
Hyperbolic Geometry in Computer Vision: A Novel Framework for Convolutional Neural Networks. CoRR abs/2303.15919 (2023) - 2022
- [j18]Hanna Drimalla, Tobias Scheffer, Niels Landwehr, Irina Baskow, Stefan Roepke, Behnoush Behnia, Isabel Dziobek:
Author Correction: Towards the automatic detection of social biomarkers in autism spectrum disorder: introducing the simulated interaction task (SIT). npj Digit. Medicine 5 (2022) - [j17]Ahmed AbdelWahab, Niels Landwehr:
Deep Distributional Sequence Embeddings Based on a Wasserstein Loss. Neural Process. Lett. 54(5): 3749-3769 (2022) - 2021
- [j16]Hamed Tavakoli, Pendar Alirezazadeh, A. Hedayatipour, A. H. Banijamali Nasib, Niels Landwehr:
Leaf image-based classification of some common bean cultivars using discriminative convolutional neural networks. Comput. Electron. Agric. 181: 105935 (2021) - [j15]Khem Raj Gautam, Guoqiang Zhang, Niels Landwehr, Julian Adolphs:
Machine learning for improvement of thermal conditions inside a hybrid ventilated animal building. Comput. Electron. Agric. 187: 106259 (2021) - [j14]Pendar Alirezazadeh, Fatemeh Rahimi-Ajdadi, Yousef Abbaspour-Gilandeh, Niels Landwehr, Hamed Tavakoli:
Improved digital image-based assessment of soil aggregate size by applying convolutional neural networks. Comput. Electron. Agric. 191: 106499 (2021) - [j13]Tibor de Camargo, Michael Schirrmann, Niels Landwehr, Karl-Heinz Dammer, Michael Pflanz:
Optimized Deep Learning Model as a Basis for Fast UAV Mapping of Weed Species in Winter Wheat Crops. Remote. Sens. 13(9): 1704 (2021) - [i7]Saddam Hijazi, Melina A. Freitag, Niels Landwehr:
POD-Galerkin reduced order models and physics-informed neural networks for solving inverse problems for the Navier-Stokes equations. CoRR abs/2112.11950 (2021) - 2020
- [j12]Hanna Drimalla, Tobias Scheffer, Niels Landwehr, Irina Baskow, Stefan Roepke, Behnoush Behnia, Isabel Dziobek:
Towards the automatic detection of social biomarkers in autism spectrum disorder: introducing the simulated interaction task (SIT). npj Digit. Medicine 3 (2020)
2010 – 2019
- 2019
- [c20]Ahmed AbdelWahab, Niels Landwehr:
Quantile Layers: Statistical Aggregation in Deep Neural Networks for Eye Movement Biometrics. ECML/PKDD (2) 2019: 332-348 - [i6]Ahmed AbdelWahab, Niels Landwehr:
Deep Distributional Sequence Embeddings Based on a Wasserstein Loss. CoRR abs/1912.01933 (2019) - 2018
- [c19]Hanna Drimalla, Niels Landwehr, Irina Baskow, Behnoush Behnia, Stefan Roepke, Isabel Dziobek, Tobias Scheffer:
Detecting Autism by Analyzing a Simulated Social Interaction. ECML/PKDD (1) 2018: 193-208 - [c18]Silvia Makowski, Lena A. Jäger, Ahmed AbdelWahab, Niels Landwehr, Tobias Scheffer:
A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movements. ECML/PKDD (1) 2018: 209-225 - [i5]Silvia Makowski, Lena A. Jäger, Ahmed AbdelWahab, Niels Landwehr, Tobias Scheffer:
A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movements. CoRR abs/1809.08031 (2018) - 2017
- [j11]Matthias Bussas, Christoph Sawade, Nicolas Kühn, Tobias Scheffer, Niels Landwehr:
Varying-coefficient models for geospatial transfer learning. Mach. Learn. 106(9-10): 1419-1440 (2017) - 2016
- [c17]Ahmed AbdelWahab, Reinhold Kliegl, Niels Landwehr:
A Semiparametric Model for Bayesian Reader Identification. EMNLP 2016: 585-594 - [e2]Paolo Frasconi, Niels Landwehr, Giuseppe Manco, Jilles Vreeken:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part I. Lecture Notes in Computer Science 9851, Springer 2016, ISBN 978-3-319-46127-4 [contents] - [e1]Paolo Frasconi, Niels Landwehr, Giuseppe Manco, Jilles Vreeken:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part II. Lecture Notes in Computer Science 9852, Springer 2016, ISBN 978-3-319-46226-4 [contents] - [i4]Ahmed AbdelWahab, Reinhold Kliegl, Niels Landwehr:
A Semiparametric Model for Bayesian Reader Identification. CoRR abs/1607.05271 (2016) - 2015
- [j10]Paul Prasse, Christoph Sawade, Niels Landwehr, Tobias Scheffer:
Learning to identify concise regular expressions that describe email campaigns. J. Mach. Learn. Res. 16: 3687-3720 (2015) - [i3]Matthias Bussas, Christoph Sawade, Tobias Scheffer, Niels Landwehr:
Varying-coefficient models with isotropic Gaussian process priors. CoRR abs/1508.07192 (2015) - 2014
- [c16]Niels Landwehr, Sebastian Arzt, Tobias Scheffer, Reinhold Kliegl:
A Model of Individual Differences in Gaze Control During Reading. EMNLP 2014: 1810-1815 - [c15]Michael Großhans, Christoph Sawade, Tobias Scheffer, Niels Landwehr:
Joint Prediction of Topics in a URL Hierarchy. ECML/PKDD (1) 2014: 514-529 - 2013
- [j9]Christoph Sawade, Steffen Bickel, Timo von Oertzen, Tobias Scheffer, Niels Landwehr:
Active evaluation of ranking functions based on graded relevance. Mach. Learn. 92(1): 41-64 (2013) - [c14]Christoph Sawade, Steffen Bickel, Timo von Oertzen, Tobias Scheffer, Niels Landwehr:
Active Evaluation of Ranking Functions Based on Graded Relevance (Extended Abstract). IJCAI 2013: 3072-3076 - 2012
- [c13]Paul Prasse, Christoph Sawade, Niels Landwehr, Tobias Scheffer:
Learning to Identify Regular Expressions that Describe Email Campaigns. ICML 2012 - [c12]Christoph Sawade, Niels Landwehr, Tobias Scheffer:
Active Comparison of Prediction Models. NIPS 2012: 1763-1771 - [c11]Christoph Sawade, Steffen Bickel, Timo von Oertzen, Tobias Scheffer, Niels Landwehr:
Active Evaluation of Ranking Functions Based on Graded Relevance. ECML/PKDD (2) 2012: 676-691 - [i2]Paul Prasse, Christoph Sawade, Niels Landwehr, Tobias Scheffer:
Learning to Identify Regular Expressions that Describe Email Campaigns. CoRR abs/1206.4637 (2012) - 2011
- [j8]Elisa Cilia, Niels Landwehr, Andrea Passerini:
Relational Feature Mining with Hierarchical Multitask kFOIL. Fundam. Informaticae 113(2): 151-177 (2011) - [j7]Ingo Thon, Niels Landwehr, Luc De Raedt:
Stochastic relational processes: Efficient inference and applications. Mach. Learn. 82(2): 239-272 (2011) - 2010
- [j6]Niels Landwehr, Andrea Passerini, Luc De Raedt, Paolo Frasconi:
Fast learning of relational kernels. Mach. Learn. 78(3): 305-342 (2010) - [c10]Christoph Sawade, Niels Landwehr, Steffen Bickel, Tobias Scheffer:
Active Risk Estimation. ICML 2010: 951-958 - [c9]Christoph Sawade, Niels Landwehr, Tobias Scheffer:
Active Estimation of F-Measures. NIPS 2010: 2083-2091
2000 – 2009
- 2009
- [j5]Niels Landwehr:
Trading expressivity for efficiency in statistical relational learning: Ph.D. thesis abstract. SIGKDD Explor. 11(2): 59-60 (2009) - 2008
- [j4]Niels Landwehr, Bernd Gutmann, Ingo Thon, Luc De Raedt, Matthai Philipose:
Relational Transformation-based Tagging for Activity Recognition. Fundam. Informaticae 89(1): 111-129 (2008) - [c8]Andreas Karwath, Kristian Kersting, Niels Landwehr:
Boosting Relational Sequence Alignments. ICDM 2008: 857-862 - [c7]Niels Landwehr:
Modeling interleaved hidden processes. ICML 2008: 520-527 - [c6]Ingo Thon, Niels Landwehr, Luc De Raedt:
A Simple Model for Sequences of Relational State Descriptions. ECML/PKDD (2) 2008: 506-521 - [p2]Kristian Kersting, Luc De Raedt, Bernd Gutmann, Andreas Karwath, Niels Landwehr:
Relational Sequence Learning. Probabilistic Inductive Logic Programming 2008: 28-55 - [p1]Niels Landwehr, Taneli Mielikäinen:
Probabilistic Logic Learning from Haplotype Data. Probabilistic Inductive Logic Programming 2008: 263-286 - 2007
- [j3]Niels Landwehr, Taneli Mielikäinen, Lauri Eronen, Hannu Toivonen, Heikki Mannila:
Constrained hidden Markov models for population-based haplotyping. BMC Bioinform. 8(S-2) (2007) - [j2]Niels Landwehr, Kristian Kersting, Luc De Raedt:
Integrating Naïve Bayes and FOIL. J. Mach. Learn. Res. 8: 481-507 (2007) - [c5]Niels Landwehr, Luc De Raedt:
r-grams: Relational Grams. IJCAI 2007: 907-912 - [i1]Matti Kääriäinen, Niels Landwehr, Sampsa Lappalainen, Taneli Mielikäinen:
Combining haplotypers. CoRR abs/0710.5116 (2007) - 2006
- [c4]Niels Landwehr, Andrea Passerini, Luc De Raedt, Paolo Frasconi:
kFOIL: Learning Simple Relational Kernels. AAAI 2006: 389-394 - 2005
- [j1]Niels Landwehr, Mark A. Hall, Eibe Frank:
Logistic Model Trees. Mach. Learn. 59(1-2): 161-205 (2005) - [c3]Niels Landwehr, Kristian Kersting, Luc De Raedt:
nFOIL: Integrating Naïve Bayes and FOIL. AAAI 2005: 795-800 - 2003
- [c2]Niels Landwehr, Mark A. Hall, Eibe Frank:
Logistic Model Trees. ECML 2003: 241-252 - 2002
- [c1]Kristian Kersting, Niels Landwehr:
Scaled Conjugate Gradients for Maximum Likelihood: An Empirical Comparison with the EM Algorithm. Probabilistic Graphical Models 2002
Coauthor Index
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