Fast Graph Representation Learning with PyTorch Geometric M Fey, JE Lenssen ICLR 2019 Workshop: Representation Learning on Graphs and Manifolds, 2019 | 4230 | 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 | 1598 | 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 | 530 | 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 | 434 | 2020 |
Deep Graph Matching Consensus M Fey, JE Lenssen, C Morris, J Masci, NM Kriege International Conference on Learning Representation (ICLR 2020), 2020 | 230 | 2020 |
Group Equivariant Capsule Networks JE Lenssen, M Fey, P Libuschewski Advances in Neural Information Processing Systems (NeurIPS 2018), 8844-8853, 2018 | 142 | 2018 |
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 | 141 | 2021 |
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 | 92 | 2020 |
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 | 73 | 2022 |
Deep Iterative Surface Normal Estimation JE Lenssen, C Osendorfer, J Masci IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2020), 2020 | 57 | 2020 |
TOCH: Spatio-Temporal Object-to-Hand Correspondence for Motion Refinement K Zhou, B Bhatnagar, JE Lenssen, G Pons-Moll European Conference on Computer Vision (ECCV 2022), 2022 | 35 | 2022 |
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 | 28 | 2017 |
Neural Parametric Gaussians for Monocular Non-Rigid Object Reconstruction D Das, C Wewer, R Yunus, E Ilg, JE Lenssen IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024), 2023 | 15 | 2023 |
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 | 13 | 2018 |
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 | 12 | 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 | 10 | 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 | 8 | 2017 |
SimNP: Learning Self-Similarity Priors Between Neural Points C Wewer, E Ilg, B Schiele, JE Lenssen International Conference on Computer Vision (ICCV), 2023 | 5 | 2023 |
Relational Deep Learning: Graph Representation Learning on Relational Databases M Fey, W Hu, K Huang, JE Lenssen, R Ranjan, J Robinson, R Ying, J You, ... arXiv preprint arXiv:2312.04615, 2023 | 4 | 2023 |
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 | 4 | 2017 |