| Fast Graph Representation Learning with PyTorch Geometric M Fey, JE Lenssen ICLR 2019 Workshop: Representation Learning on Graphs and Manifolds, 2019 | 2365 | 2019 |
| Weisfeiler and leman go neural: Higher-order graph neural networks C Morris, M Ritzert, M Fey, WL Hamilton, JE Lenssen, G Rattan, M Grohe Proceedings of the AAAI Conference on Artificial Intelligence 33, 4602-4609, 2019 | 930 | 2019 |
| SplineCNN: Fast geometric deep learning with continuous B-spline kernels M Fey, JE Lenssen, F Weichert, H Müller IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), 869-877, 2018 | 394 | 2018 |
| Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction R Chabra, JE Lenssen, E Ilg, T Schmidt, J Straub, S Lovegrove, ... European Conference on Computer Vision (ECCV 2020), 2020 | 242 | 2020 |
| Deep Graph Matching Consensus M Fey, JE Lenssen, C Morris, J Masci, NM Kriege International Conference on Learning Representation (ICLR 2020), 2020 | 160 | 2020 |
| Group Equivariant Capsule Networks JE Lenssen, M Fey, P Libuschewski Advances in Neural Information Processing Systems (NeurIPS 2018), 8844-8853, 2018 | 114 | 2018 |
| Quaternion Equivariant Capsule Networks for 3D Point Clouds Y Zhao, T Birdal, JE Lenssen, E Menegatti, L Guibas, F Tombari European Conference on Computer Vision (ECCV 2020), 2020 | 75 | 2020 |
| Gnnautoscale: Scalable and expressive graph neural networks via historical embeddings M Fey, JE Lenssen, F Weichert, J Leskovec International Conference on Machine Learning (ICML 2021), 3294-3304, 2021 | 55 | 2021 |
| Deep Iterative Surface Normal Estimation JE Lenssen, C Osendorfer, J Masci IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2020), 2020 | 29* | 2020 |
| Application of the PAMONO-sensor for quantification of microvesicles and determination of nano-particle size distribution V Shpacovitch, I Sidorenko, JE Lenssen, V Temchura, F Weichert, ... Sensors 17 (2), 244, 2017 | 22 | 2017 |
| Real-time Low SNR Signal Processing for Nanoparticle Analysis with Deep Neural Networks. JE Lenssen, A Toma, A Seebold, V Shpacovitch, P Libuschewski, ... BIOSIGNALS, 36-47, 2018 | 12 | 2018 |
| Pose-NDF: Modeling Human Pose Manifolds with Neural Distance Fields G Tiwari, D Antic, JE Lenssen, N Sarafianos, T Tung, G Pons-Moll European Conference on Computer Vision (ECCV 2022), 2022 | 11 | 2022 |
| Nanoparticle Classification Using Frequency Domain Analysis on Resource-Limited Platforms M Yayla, A Toma, KH Chen, JE Lenssen, V Shpacovitch, R Hergenröder, ... Sensors 19 (19), 4138, 2019 | 9 | 2019 |
| Adaptive Quality Optimization of Computer Vision Tasks in Resource-Constrained Devices using Edge Computing A Toma, J Wenner, JE Lenssen, JJ Chen 2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid …, 2019 | 8 | 2019 |
| Real-time virus size classification using surface plasmon pamono resonance and convolutional neural networks JE Lenssen, V Shpacovitch, F Weichert Bildverarbeitung für die Medizin 2017: Algorithmen-Systeme-Anwendungen …, 2017 | 7 | 2017 |
| TOCH: Spatio-Temporal Object Correspondence to Hand for Motion Refinement K Zhou, B Bhatnagar, JE Lenssen, G Pons-Moll European Conference on Computer Vision (ECCV 2022), 2022 | 5* | 2022 |
| A Review of Nano-Particle Analysis with the PAMONO-Sensor JE Lenssen, V Shpacovitch, D Siedhoff, P Libuschewski, R Hergenröder, ... Biosens. Adv. Rev 1, 81-100, 2017 | 3 | 2017 |
| Resource-Efficient Nanoparticle Classification Using Frequency Domain Analysis M Yayla, A Toma, JE Lenssen, V Shpacovitch, KH Chen, F Weichert, ... Bildverarbeitung für die Medizin 2019: Algorithmen–Systeme–Anwendungen …, 2019 | 2 | 2019 |
| Saving Energy for Cloud Applications in Mobile Devices Using Nearby Resources A Toma, A Starinow, JE Lenssen, JJ Chen 2018 26th Euromicro International Conference on Parallel, Distributed and …, 2018 | 2 | 2018 |
| Differentiable Algorithms with Data-driven Parameterization in 3D Vision JE Lenssen Dissertation, 2022 | | 2022 |
Matthias FeyFounding Engineer @ kumo.aiVerified email at tu-dortmund.de
