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Results: 57,911
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  • Najdenkoska, I., Zhen, X., Worring, M., & Shao, L. (2021). Variational Topic Inference for Chest X-Ray Report Generation. In M. de Bruijne, P. C. Cattin, S. Cotin, N. Padoy, S. Speidel, Y. Zheng, & C. Essert (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021 : proceedings (Vol. III, pp. 625-635). (Lecture Notes in Computer Science; Vol. 12903). Springer. https://doi.org/10.1007/978-3-030-87199-4_59
  • van Sonsbeek, T., Zhen, X., Worring, M., & Shao, L. (2021). Variational Knowledge Distillation for Disease Classification in Chest X-Rays. In A. Feragen, S. Sommer, J. Schnabel, & M. Nielsen (Eds.), Information Processing in Medical Imaging: 27th International Conference, IPMI 2021, virtual event, June 28–June 30, 2021 : proceedings (pp. 334-345). (Lecture Notes in Computer Science; Vol. 12729). Springer. https://doi.org/10.1007/978-3-030-78191-0_26
  • Vasconcelos, V. V., Constantino, S. M., Dannenberg, A., Lumkowsky, M., Weber, E., & Levin, S. (2021). Segregation and clustering of preferences erode socially beneficial coordination. Proceedings of the National Academy of Sciences of the United States of America, 118(50), Article e2102153118. https://doi.org/10.1073/pnas.2102153118
  • Zhang, L., Zuo, L., Du, Y., & Zhen, X. (2021). Learning to Adapt with Memory for Probabilistic Few-Shot Learning. IEEE transactions on circuits and systems for video technology, 31(11), 4283-4292. https://doi.org/10.1109/TCSVT.2021.3052785
  • Salehi, M., Arya, A., Pajoum, B., Otoofi, M., Shaeiri, A., Rohban, M. H., & Rabiee, H. R. (2021). ARAE: Adversarially robust training of autoencoders improves novelty detection. Neural Networks, 144, 726-736. https://doi.org/10.1016/j.neunet.2021.09.014
  • Sosnovik, I., Moskalev, A., & Smeulders, A. (2021). Scale Equivariance Improves Siamese Tracking. In 2021 IEEE Winter Conference on Applications of Computer Vision: proceedings : 5-9 January 2021, virtual event (pp. 2764-2773). (WACV). IEEE Computer Society. https://doi.org/10.1109/WACV48630.2021.00281
  • Alam, M., Ali, M., Groth, P., Hitzler, P., Lehmann, J., Paulheim, H., Rettinger, A., Sack, H., Sadeghi, A., & Tresp, V. (Eds.) (2021). Machine Learning with Symbolic Methods and Knowledge Graphs: co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021) : Virtual, September 17, 2021. (CEUR Workshop Proceedings; Vol. 2997). CEUR-WS. http://ceur-ws.org/Vol-2997
  • Zhang, Y., Wang, L., Wang, D., Qi, J., & Lu, H. (2021). Learning Regression and Verification Networks for Robust Long-term Tracking. International Journal of Computer Vision, 129(9), 2536–2547. https://doi.org/10.1007/s11263-021-01487-3
  • Mohseni, N., & Bol, R. (2021). Variation in the rate of land subsidence induced by groundwater extraction and its effect on the response pattern of soil microbial communities. Earth Surface Processes and Landforms, 46(10), 1898-1908. https://doi.org/10.1002/esp.5133
  • Broekaart, D. W. M., Korotkov, A., Gorter, J. A., & van Vliet, E. A. (2021). Perivascular inflammation and extracellular matrix alterations in blood-brain barrier dysfunction and epilepsy. In D. Jangiro, A. Nehlig, & N. Marchi (Eds.), Inflammation and epilepsy: new vistas (pp. 71-106). (Progress in Infammation Research; Vol. 88). Springer. https://doi.org/10.1007/978-3-030-67403-8_4
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