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Suk, J., de Haan, P., Lippe, P., Brune, C., & Wolterink, J. M. (2024). Mesh neural networks for SE(3)-equivariant hemodynamics estimation on the artery wall. Computers in Biology and Medicine, 173, Article 108328. Advance online publication. https://doi.org/10.1016/j.compbiomed.2024.108328
2023
Brehmer, J., Cohen, T., De Haan, P., & Lippe, P. (2023). Weakly supervised causal representation learning. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), 36th Conference on Neural Information Processing Systems (NeurIPS 2022): New Orleans, Louisiana, USA, 28 November-9 December 2022 (Vol. 50, pp. 38319-38331). (Advances in Neural Information Processing Systems; Vol. 35). Neural Information Processing Systems Foundation. https://doi.org/10.48550/arXiv.2203.16437[details]
Lippe, P., Magliacane, S., Löwe, S., Asano, Y. M., Cohen, T., & Gavves, E. (2023). Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems. In The Eleventh International Conference on Learning Representations https://openreview.net/forum?id=itZ6ggvMnzS
Lippe, P., Veeling, B. S., Perdikaris, P., Turner, R. E., & Brandstetter, J. (2023). PDE-Refiner: Achieving Accurate Long Rollouts with Temporal Neural PDE Solvers. In Thirty-seventh Conference on Neural Information Processing Systems Neural Information Processing Systems Foundation. https://arxiv.org/abs/2308.05732
Löwe, S., Lippe, P., Locatello, F., & Welling, M. (in press). Rotating Features for Object Discovery. In Thirty-seventh Conference on Neural Information Processing Systems Neural Information Processing Systems Foundation. https://doi.org/10.48550/ARXIV.2306.00600
Pervez, A., Lippe, P., & Gavves, E. (2023). Differentiable Mathematical Programming for Object-Centric Representation Learning. In International Conference on Learning Representations https://openreview.net/forum?id=1J-ZTr7aypY
Pervez, A., Lippe, P., & Gavves, E. (2023). Scalable Subset Sampling with Neural Conditional Poisson Networks. In International Conference on Learning Representations https://openreview.net/forum?id=p8hMBcPtvju
2022
Langedijk, A., Dankers, V., Lippe, P., Bos, S., Cardenas Guevara, B., Yannakoudakis, H., & Shutova, E. (2022). Meta-learning for fast cross-lingual adaptation in dependency parsing. In S. Muresan, P. Nakov, & A. Villavicencio (Eds.), The 60th Annual Meeting of the Association for Computational Linguistics: ACL 2022 : proceedings of the conference : May 22-27, 2022 (Vol. 1, pp. 8503–8520). Association for Computational Linguistics. https://doi.org/10.48550/arXiv.2104.04736, https://doi.org/10.18653/v1/2022.acl-long.582[details]
Lippe, P., Cohen, T., & Gavves, S. (2022). Efficient Neural Causal Discovery without Acyclicity Constraints. In International Conference on Learning Representations https://openreview.net/forum?id=eYciPrLuUhG
Lippe, P., Magliacane, S., Löwe, S., Asano, Y. M., Cohen, T., & Gavves, E. (2022). CITRIS: Causal Identifiability from Temporal Intervened Sequences. Proceedings of Machine Learning Research, 162, 13557-13603. https://proceedings.mlr.press/v162/lippe22a.html[details]
Lippe, P., Ren, P., Haned, H., Voorn, B., & de Rijke, M. (2022). Simultaneously Improving Utility and User Experience in Task-oriented Dialogue Systems. In eCom 2022: The SIGIR 2022 SIGIR Workshop on eCommerce ACM. https://sigir-ecom.github.io/ecom22Papers/paper_5042.pdf
Löwe, S., Lippe, P., Rudolph, M., & Welling, M. (2022). Complex-Valued Autoencoders for Object Discovery. Transactions on Machine Learning Research, Article 428. https://openreview.net/forum?id=1PfcmFTXoa[details]
Suk, J., de Haan, P., Lippe, P., Brune, C., & Wolterink, J. M. (2022). Mesh Convolutional Neural Networks for Wall Shear Stress Estimation in 3D Artery Models. In E. Puyol Antón, M. Pop, C. Martín-Isla, M. Sermesant, A. Suinesiaputra, O. Camara, K. Lekadir, & A. Young (Eds.), Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge: 12th International Workshop, STACOM 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021 : revised selected papers (pp. 93-102). (Lecture Notes in Computer Science; Vol. 13131). Springer. https://doi.org/10.1007/978-3-030-93722-5_11[details]
2021
Kiela, D., Firooz, H., Mohan, A., Goswami, V., Singh, A., Fitzpatrick, C. A., Bull, P., Lipstein, G., Nelli, T., Zhu, R., Muennighoff, N., Velioglu, R., Rose, J., Lippe, P., Holla, N., Chandra, S., Rajamanickam, S., Antoniou, G., Shutova, E. V., ... Parikh, D. (2021). The Hateful Memes Challenge: Competition Report. Proceedings of Machine Learning Research, 133, 344-360. http://proceedings.mlr.press/v133/kiela21a.html
Lippe, P., & Gavves, E. (2021). Categorical Normalizing Flows via Continuous Transformations. In International Conference on Learning Representations https://openreview.net/pdf?id=-GLNZeVDuik
2023
Lippe, P., Magliacane, S., Löwe, S., Asano, Y. M., Cohen, T., & Gavves, E. (2023). BISCUIT: Causal Representation Learning from Binary Interactions. Proceedings of Machine Learning Research, 216, 1263-1273. https://proceedings.mlr.press/v216/lippe23a.html[details]
Lippe, P., & Gavves, E. (2021). Categorical Normalizing Flows via Continuous Transformations. Paper presented at 9th International Conference on Learning Representations, virtual. https://openreview.net/pdf?id=-GLNZeVDuik
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