Search results
Results: 80,477
Number of items: 80,477
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Dankers, V., & Titov, I. (2022). Recursive Neural Networks with Bottlenecks Diagnose (Non-)Compositionality. In Y. Goldberg, Z. Kozareva, & Y. Zhang (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2022: Conference on Empirical Methods in Natural Language Processing (EMNLP), Abu Dhabi, United Arab Emirates, 7-11 December 2022 (pp. 4361–4378). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.findings-emnlp.320 -
Rempel, A., & Gupta, J. (2022). Equitable, effective, and feasible approaches for a prospective fossil fuel transition. Wiley Interdisciplinary Reviews. Climate Change, 13(2), Article e756. https://doi.org/10.1002/wcc.756
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Ye, D., Zun, P., Krzhizhanovskaya, V., & Hoekstra, A. G. (2022). Uncertainty quantification of a three-dimensional in-stent restenosis model with surrogate modelling. Journal of the Royal Society Interface, 19(187), Article 20210864. https://doi.org/10.1098/rsif.2021.0864
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Kaiser, B. N., Gurung, D., Rai, S., Bhardwaj, A., Dhakal, M., Cafaro, C. L., Sikkema, K. J., Lund, C., Patel, V., Jordans, M. J. D., Luitel, N. P., & Kohrt, B. A. (2022). Mechanisms of action for stigma reduction among primary care providers following social contact with service users and aspirational figures in Nepal: an explanatory qualitative design. International Journal of Mental Health Systems, 16(1), Article 37. https://doi.org/10.1186/s13033-022-00546-7
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Li, H., Younes, S. H. H., Chen, S., Duan, P., Cui, C., Wever, R., Zhang, W., & Hollmann, F. (2022). Chemoenzymatic Hunsdiecker-Type Decarboxylative Bromination of Cinnamic Acids. ACS Catalysis, 12(8), 4554-4559. https://doi.org/10.1021/acscatal.2c00485
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Mandjes, M., & Sollie, B. (2022). A numerical approach to evaluating the transient distribution of a quasi birth-death process. Methodology and Computing in Applied Probability, 24(3), 1693-1715. https://doi.org/10.1007/s11009-021-09882-6
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AkbarTajari, M., Rajaee, S., & Pilehvar, M. T. (2022). An Empirical Study on the Transferability of Transformer Modules in Parameter-Efficient Fine-Tuning. In Y. Goldberg, Z. Kozareva, & Y. Zhang (Eds.), The 2022 Conference on Empirical Methods in Natural Language Processing : Proceedings of the Conference: EMNLP 2022 : December 7-11, 2022 (pp. 10617–10625). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.emnlp-main.726
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Evans, G. W., Hommes, C., McGough, B., & Salle, I. (2022). Are long-horizon expectations (de-)stabilizing? Theory and experiments. Journal of Monetary Economics, 132, 44-63. https://doi.org/10.1016/j.jmoneco.2022.08.002
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Nguyen, D.-K., Yu, J., Booij, O., Oswald, M. R., & Snoek, C. G. M. (2022). BoxeR: Box-Attention for 2D and 3D Transformers. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition: New Orleans, Louisiana, 19-24 June 2022 : proceedings (pp. 4763-4772). (CVPR). IEEE Computer Society. https://doi.org/10.48550/arXiv.2111.13087, https://doi.org/10.1109/CVPR52688.2022.00473
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