Hebert, L., Golab, L., Poupart, P., & Cohen, R. (2022). FedFormer: Contextual Federation With Attention in Reinforcement Learning ArXiv, abs/2205.13697. https://doi.org/10.48550/arXiv.2205.13697
References
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Peng, P., Ozsu, T., Zou, L., Yan, C., & Liu, C. (2022). MPC: Minimum Property-Cut RDF Graph Partitioning Presented at the MPC: Minimum Property-Cut RDF Graph Partitioning conference. https://doi.org/10.1109/ICDE53745.2022.00019
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Xue, H., Salim, F. D., Ren, Y., & Clarke, C. (2022). Translating Human Mobility Forecasting Through Natural Language Generation Presented at the Translating Human Mobility Forecasting Through Natural Language Generation conference. https://doi.org/10.1145/3488560.3498387
Ilyas, I., & Rekatsinas, T. (2022). Machine Learning and Data Cleaning: Which Serves the Other? Journal of Data and Information Quality, 14, 1-13. https://doi.org/10.1145/3506712
Ma, X., Pradeep, R., Nogueira, R., & Lin, J. (2022). Document Expansion Baselines and Learned Sparse Lexical Representations For MS MARCO V1 and V2 Presented at the Expansion Baselines and Learned Sparse Lexical Representations For MS MARCO V1 and V2 conference. https://doi.org/10.1145/3477495.3531749
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Nanayakkara, P., Bater, J., He, X., Hullman, J., & Rogers, J. (2022). Visualizing Privacy-Utility Trade-Offs in Differentially Private Data Releases ArXiv, abs/2201.05964. Retrieved from https://arxiv.org/abs/2201.05964
Zhang, X., Ogueji, K., Ma, X., & Lin, J. (2022). Towards Best Practices for Training Multilingual Dense Retrieval Models ArXiv, abs/2204.02363. https://doi.org/10.48550/arXiv.2204.02363