| Structure-aware transformer for graph representation learning D Chen, L O’Bray, K Borgwardt International conference on machine learning, 3469-3489, 2022 | 359 | 2022 |
| Graphit: Encoding graph structure in transformers G Mialon, D Chen, M Selosse, J Mairal arXiv preprint arXiv:2106.05667, 2021 | 201 | 2021 |
| A kernel perspective for regularizing deep neural networks A Bietti, G Mialon, D Chen, J Mairal International Conference on Machine Learning, 664-674, 2019 | 83 | 2019 |
| Convolutional kernel networks for graph-structured data D Chen, L Jacob, J Mairal International Conference on Machine Learning, 1576-1586, 2020 | 76 | 2020 |
| A trainable optimal transport embedding for feature aggregation and its relationship to attention G Mialon, D Chen, A d'Aspremont, J Mairal arXiv preprint arXiv:2006.12065, 2020 | 70* | 2020 |
| Metamixup: Learning adaptive interpolation policy of mixup with metalearning Z Mai, G Hu, D Chen, F Shen, HT Shen IEEE transactions on neural networks and learning systems 33 (7), 3050-3064, 2021 | 46 | 2021 |
| Biological sequence modeling with convolutional kernel networks D Chen, L Jacob, J Mairal Bioinformatics 35 (18), 3294-3302, 2019 | 40 | 2019 |
| Recurrent kernel networks D Chen, L Jacob, J Mairal Advances in Neural Information Processing Systems 32, 2019 | 24 | 2019 |
| Endowing protein language models with structural knowledge D Chen, P Hartout, P Pellizzoni, C Oliver, K Borgwardt arXiv preprint arXiv:2401.14819, 2024 | 13 | 2024 |
| Proteinshake: Building datasets and benchmarks for deep learning on protein structures T Kucera, C Oliver, D Chen, K Borgwardt Advances in Neural Information Processing Systems 36, 58277-58289, 2023 | 11 | 2023 |
| Unsupervised manifold alignment with joint multidimensional scaling D Chen, B Fan, C Oliver, K Borgwardt arXiv preprint arXiv:2207.02968, 2022 | 7 | 2022 |
| HTR-VT: Handwritten text recognition with vision transformer Y Li, D Chen, T Tang, X Shen Pattern Recognition 158, 110967, 2025 | 6 | 2025 |
| Approximate network motif mining via graph learning C Oliver, D Chen, V Mallet, P Philippopoulos, K Borgwardt arXiv preprint arXiv:2206.01008, 2022 | 5 | 2022 |
| Learning long range dependencies on graphs via random walks D Chen, TH Schulz, K Borgwardt arXiv preprint arXiv:2406.03386, 2024 | 4 | 2024 |
| SURE: SUrvey REcipes for building reliable and robust deep networks Y Li, Y Chen, X Yu, D Chen, X Shen Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 4 | 2024 |
| Fisher information embedding for node and graph learning D Chen, P Pellizzoni, K Borgwardt International Conference on Machine Learning, 4839-4855, 2023 | 4 | 2023 |
| Predicting in vitro single-neuron firing rates upon pharmacological perturbation using Graph Neural Networks T Kim, D Chen, P Hornauer, V Emmenegger, J Bartram, S Ronchi, ... Frontiers in Neuroinformatics 16, 1032538, 2023 | 4 | 2023 |
| GraphiT: Encoding Graph Structure in Transformers. CoRR abs/2106.05667 (2021) G Mialon, D Chen, M Selosse, J Mairal arXiv preprint arXiv:2106.05667, 2021 | 4 | 2021 |
| Biomarker identification by interpretable maximum mean discrepancy MF Adamer, SC Brüningk, D Chen, K Borgwardt Bioinformatics 40 (Supplement_1), i501-i510, 2024 | 2 | 2024 |
| On the expressivity and sample complexity of node-individualized graph neural networks P Pellizzoni, TH Schulz, D Chen, K Borgwardt The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024 | 2 | 2024 |
Karsten BorgwardtDirector, Max Planck Institute of BiochemistryVerified email at biochem.mpg.de
