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Results: 24
Number of items: 24
  • Open Access
    Romero, D. W., Bekkers, E. J., Tomczak, J. M., & Hoogendoorn, M. (2020). Wavelet networks: Scale equivariant learning from raw waveforms. (v1 ed.) ArXiv. https://arxiv.org/abs/2006.05259
  • Open Access
    Ilse, M., Tomczak, J. M., & Forré, P. (2020). Selecting Data Augmentation for Simulating Interventions. (v4 ed.) ArXiv. https://arxiv.org/abs/2005.01856
  • Open Access
    Oh, C., Tomczak, J. M., Gavves, E., & Welling, M. (2020). Combinatorial Bayesian Optimization using the Graph Cartesian Product. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, & R. Garnett (Eds.), 32nd Conference on Neural Information Processing Systems (NeurIPS 2019): Vancouver, Canada, 8-14 December 2019 (Vol. 4, pp. 2891-2901). (Advances in Neural Information Processing Systems; Vol. 32). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2019/hash/2cb6b10338a7fc4117a80da24b582060-Abstract.html
  • Open Access
    Romero, D. W., Bekkers, E. J., Tomczak, J. M., & Hoogendoorn, M. (2020). Attentive group equivariant convolutional networks. Proceedings of Machine Learning Research, 119, 8188-8199. http://proceedings.mlr.press/v119/romero20a.html
  • Open Access
    Tomczak, J. M., & Węglarz-Tomczak, E. (2019). Estimating kinetic constants in the Michaelis-Menten model from one enzymatic assay using Approximate Bayesian Computation. FEBS Letters, 593(19), 2742-2750. https://doi.org/10.1002/1873-3468.13531
  • Open Access
    Davidson, T. R., Tomczak, J. M., & Gavves, E. (2019). Increasing Expressivity of a Hyperspherical VAE. Paper presented at Bayesian Deep Learning Workshop, Vancouver, British Columbia, Canada. https://doi.org/10.48550/arXiv.1910.02912
  • Tomczak, J. (2018, March 21). Histopathology data of bone marrow biopsies (HistBMP or HistMNIST) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.1205024
  • Open Access
    Davidson, T. R., Falorsi, L., De Cao, N., Kipf, T., & Tomczak, J. M. (2018). Hyperspherical Variational Auto-Encoders. In A. Globerson, & R. Silva (Eds.), Uncertainty in Artificial Intelligence: proceedings of the Thirty-Fourth Concerence (2018) : August 6-10, 2018, Monterey, California, USA (pp. 856-865). AUAI Press. http://auai.org/uai2018/proceedings/papers/309.pdf
  • Open Access
    Tomczak, J. M., & Welling, M. (2018). VAE with a VampPrior. Proceedings of Machine Learning Research, 84, 1214-1223. https://arxiv.org/abs/1705.07120
  • Open Access
    van den Berg, R., Hasenclever, L., Tomczak, J. M., & Welling, M. (2018). Sylvester Normalizing Flows for Variational Inference. In A. Globerson, & R. Silva (Eds.), Uncertainty in Artificial Intelligence: proceedings of the Thirty-Fourth Concerence (2018) : August 6-10, 2018, Monterey, California, USA (pp. 393-402). AUAI Press. http://auai.org/uai2018/proceedings/papers/156.pdf
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