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Results: 27
Number of items: 27
  • Open Access
    Knigge, D. M., Romero, D. W., Gu, A., Gavves, E., Bekkers, E. J., Tomczak, J. M., Hoogendoorn, M., & Sonke, J.-J. (2023). Modelling Long Range Dependencies in N-D: From Task-Specific to a General Purpose CNN. In The Eleventh International Conference on Learning Representations: ICLR 2023 https://openreview.net/forum?id=ZW5aK4yCRqU
  • Open Access
    van Krieken, E., Thanapalasingam, T., Tomczak, J. M., van Harmelen, F., & ten Teije, A. (2023). A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), 37th Conference on Neural Information Processing Systems (NeurIPS 2023): 10-16 December 2023, New Orleans, Louisana, USA (pp. 24586-24609). (Advances in Neural Information Processing Systems; Vol. 36). Neural Information Processing Systems Foundation. https://doi.org/10.52202/075280-1069
  • Open Access
    Ilse, M. (2022). Invariance in deep representations. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Romero, D. W., Bruintjes, R.-J., Bekkers, E. J., Tomczak, J. M., Hoogendoorn, M., & van Gemert, J. C. (2022). FlexConv: Continuous Kernel Convolutions With Differentiable Kernel Sizes. In The Tenth International Conference on Learning Representations: ICLR 2022 https://openreview.net/forum?id=3jooF27-0Wy
  • Open Access
    Romero, D. W., Kuzina, A., Bekkers, E. J., Tomczak, J. M., & Hoogendoorn, M. (2022). CKConv: Continuous Kernel Convolution For Sequential Data. In The Tenth International Conference on Learning Representations: ICLR 2022 https://doi.org/10.48550/arXiv.2102.02611
  • Lavitt, F., Rijlaarsdam, D. J., Linden, D. v. d., Weglarz-Tomczak, E., & Tomczak, J. (2021). Microscope images of human cancer cell lines (U2OS and HL-60) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4428844
  • Open Access
    Weglarz-Tomczak, E., Tomczak, J. M., Eiben, A. E., & Brul, S. (2021). Population-based parameter identification for dynamical models of biological networks with an application to Saccharomyces cerevisiae. Processes, 9(1), Article 98. https://doi.org/10.3390/pr9010098
  • Open Access
    Weglarz-Tomczak, E., Tomczak, J. M., Talma, M., Burda-Grabowska, M., Giurg, M., & Brul, S. (2021). Identification of ebselen and its analogues as potent covalent inhibitors of papain-like protease from SARS-CoV-2. Scientific Reports, 11(1), Article 3640. https://doi.org/10.1038/s41598-021-83229-6
  • Open Access
    Hoogeboom, E., Garcia Satorras, V., Tomczak, J., & Welling, M. (2021). The Convolution Exponential and Generalized Sylvester Flows. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), 34th Concerence on Neural Information Processing Systems (NeurIPS 2020): online, 6-12 December 2020 (Vol. 22, pp. 18249-18248). (Advances in Neural Information Processing Systems; Vol. 33). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2020/hash/d3f06eef2ffac7faadbe3055a70682ac-Abstract.html
  • Open Access
    Ilse, M., Tomczak, J. M., & Forré, P. (2021). Selecting Data Augmentation for Simulating Interventions. Proceedings of Machine Learning Research, 139, 4555-4562. https://proceedings.mlr.press/v139/ilse21a.html
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