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Results: 58,357
Number of items: 58,357
  • Open Access
    Biggs, T. E. G., Rozema, P. D., Evans, C., Timmermans, K. R., Meredith, M. P., Pond, D. W., & Brussaard, C. P. D. (2022). Control of Antarctic phytoplankton community composition and standing stock by light availability. Polar Biology, 45(11), 1635–1653. https://doi.org/10.1007/s00300-022-03094-5
  • Open Access
    Weinland, D. H., van der Maas, K., Wang, Y., Bottega Pergher, B., van Putten, R. J., Wang, B., & Gruter, G. J. M. (2022). Overcoming the low reactivity of biobased, secondary diols in polyester synthesis. Nature Communications, 13, Article 7370. https://doi.org/10.1038/s41467-022-34840-2
  • Open Access
    Ülger, O., Wiederer, J., Ghafoorian, M., Belagiannis, V., & Mettes, P. (2022). Multi-Task Edge Prediction in Temporally-Dynamic Video Graphs. In The 33rd British Machine Vision Conference Proceedings: BMVC 2022 : 21st-24th November 2022, London, UK Article 968 BMVA Press. https://bmvc2022.mpi-inf.mpg.de/968/
  • Open Access
    Ventouri, I. K., Loeber, S., Somsen, G. W., Schoenmakers, P. J., & Astefanei, A. (2022). Field-flow fractionation for molecular-interaction studies of labile and complex systems: A critical review. Analytica Chimica Acta, 1193, Article 339396. https://doi.org/10.1016/j.aca.2021.339396
  • Open Access
    Antorán, J., Janz, D., Allingham, J. U., Daxberger, E., Barbano, R., Nalisnick, E., & Hernández-Lobato, J. M. (2022). Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. Proceedings of Machine Learning Research, 162, 796-821. https://doi.org/10.48550/arXiv.2206.08900
  • Open Access
    Pradeep, R., Liu, Y., Zhang, X., Li, Y., Yates, A., & Lin, J. (2022). Squeezing Water from a Stone: A Bag of Tricks for Further Improving Cross-Encoder Effectiveness for Reranking. In M. Hagen, S. Verberne, C. Macdonald, C. Seifert, K. Balog, K. Nørvåg, & V. Setty (Eds.), Advances in Information Retrieval: 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10–14, 2022 : proceedings (Vol. I, pp. 655–670). (Lecture Notes in Computer Science; Vol. 13185). Springer. https://doi.org/10.1007/978-3-030-99736-6_44
  • Open Access
    Rentier, E. S., & Cammeraat, L. H. (2022). The environmental impacts of river sand mining. Science of the Total Environment, 838(1), Article 155877. https://doi.org/10.1016/j.scitotenv.2022.155877
  • Open Access
    Martins, A. F. T., Treviso, M., Farinhas, A., Aguiar, P. M. Q., Figueiredo, M. A. T., Blondel, M., & Niculae, V. (2022). Sparse continuous distributions and Fenchel-Young losses. Journal of Machine Learning Research, 23, Article 257. https://www.jmlr.org/papers/v23/21-0879.html
  • Open Access
    Sörensen, L. K. A., Bohté, S. M., Slagter, H. A., & Scholte, H. S. (2022). Arousal state affects perceptual decisionmaking by modulating hierarchical sensory processing in a large-scale visual system model. PLoS Computational Biology, 18(4), Article e1009976. https://doi.org/10.1371/journal.pcbi.1009976
  • Open Access
    Groenendijk, R., Dorst, L., & Gevers, T. (2022). MorphPool: Efficient Non-linear Pooling & Unpooling in CNNs. In The 33rd British Machine Vision Conference Proceedings: BMVC 2022 : 21st-24th November 2022, London, UK Article 56 BMVA Press. https://doi.org/10.48550/arXiv.2211.14037
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