Lin, J. (2022). On the Interaction Between Differential Privacy and Gradient Compression In Deep Learning ArXiv, abs/2211.00734. https://doi.org/10.48550/arXiv.2211.00734
References
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2022
Yang, M. Y. R., Yang, S., & Lin, J. (2022). Integration of Text and Geospatial Search for Hydrographic Datasets Using the Lucene Search Library Presented at the Integration of Text and Geospatial Search for Hydrographic Datasets Using the Lucene Search Library conference. https://doi.org/10.1145/3529372.3533280
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
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 Document Expansion Baselines and Learned Sparse Lexical Representations For MS MARCO V1 and V2 conference. https://doi.org/10.1145/3477495.3531749
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
Li, H., Wang, S., Zhuang, S., Mourad, A., Ma, X., Lin, J., & Zuccon, G. (2022). To Interpolate or Not to Interpolate: PRF, Dense and Sparse Retrievers Presented at the To Interpolate or Not to Interpolate: PRF, Dense and Sparse Retrievers conference. https://doi.org/10.1145/3477495.3531884
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
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
Tang, R., Kumar, K., Yang, G., Pandey, A., Mao, Y., Belyaev, V., … Lin, J. (2022). SpeechNet: Weakly Supervised, End-to-End Speech Recognition at Industrial Scale Presented at the SpeechNet: Weakly Supervised, End-to-End Speech Recognition at Industrial Scale conference. Retrieved from https://aclanthology.org/2022.emnlp-industry.29
Parsa, M. S., Shi, H., Xu, Y., Yim, A., Yin, Y., & Golab, L. (2022). Analyzing Climate Change Discussions on Reddit Presented at the Analyzing Climate Change Discussions on Reddit conference. https://doi.org/10.1109/CSCI58124.2022.00150