Li, H., Zhuang, S., Ma, X., Lin, J., & Zuccon, G. (2022). Pseudo-Relevance Feedback With Dense Retrievers in Pyserini Presented at the Pseudo-Relevance Feedback With Dense Retrievers in Pyserini conference. https://doi.org/10.1145/3572960.3572982
References
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2022
Zhong, W., Xie, Y., & Lin, J. (2022). Applying Structural and Dense Semantic Matching for the ARQMath Lab 2022, Clef Presented at the Applying Structural and Dense Semantic Matching for the ARQMath Lab 2022, Clef conference. Retrieved from http://ceur-ws.org/Vol-3180/paper-09.pdf
Wang, R., Wang, J., Idreos, S., Ozsu, T., & Aref, W. G. (2022). The Case for Distributed Shared-Memory Databases With RDMA-Enabled Memory Disaggregation ArXiv, abs/2207.03027. https://doi.org/10.48550/arXiv.2207.03027
Shi, P., Song, L., Jin, L., Mi, H., Bai, H., Lin, J., & Yu, D. (2022). Cross-Lingual Text-to-SQL Semantic Parsing With Representation Mixup Presented at the Cross-Lingual Text-to-SQL Semantic Parsing With Representation Mixup conference. Retrieved from https://aclanthology.org/2022.findings-emnlp.388
Kalavri, V., & Salihoglu, S. (2022). GRADES-NDA\textquoteright22: 5th International Workshop on Graph Data Management Experiences and Systems (GRADES) and Network Data Analytics (NDA) Presented at the GRADES-NDA\textquoteright22: 5th International Workshop on Graph Data Management Experiences and Systems (GRADES) and Network Data Analytics (NDA) conference. https://doi.org/10.1145/3514221.3524074
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
Durvasula, S., Kiguru, R., Mathur, S., Xu, J., Lin, J., & Vijaykumar, N. (2022). VoxelCache: Accelerating Online Mapping in Robotics and 3D Reconstruction Tasks Presented at the VoxelCache: Accelerating Online Mapping in Robotics and 3D Reconstruction Tasks conference. https://doi.org/10.1145/3559009.3569675
Ghayyur, S., Ghosh, D., He, X., & Mehrotra, S. (2022). MIDE: Accuracy Aware Minimally Invasive Data Exploration for Decision Support Proceedings of the VLDB Endowment (PVLDB), 15, 2653-2665. Retrieved from https://www.vldb.org/pvldb/vol15/p2653-ghayyur.pdf
Kane, A., Ng, Y. K., & Tompa, F. (2022). Dowsing for Answers to Math Questions: Doing Better With Less Presented at the Dowsing for Answers to Math Questions: Doing Better With Less conference. Retrieved from http://ceur-ws.org/Vol-3180/paper-03.pdf
Ammar, K., Sahu, S., Salihoglu, S., & Ozsu, T. (2022). Optimizing Differentially-Maintained Recursive Queries on Dynamic Graphs Proceedings of the VLDB Endowment (PVLDB), 15, 3186-3198. Retrieved from https://www.vldb.org/pvldb/vol15/p3186-ammar.pdf