Reference author: Jimmy Lin

Lin, J. (2022). Building a Culture of Reproducibility in Academic Research ArXiv, abs/2212.13534. https://doi.org/10.48550/arXiv.2212.13534

Gao, L., Ma, X., Lin, J., & Callan, J. (2022). Precise Zero-Shot Dense Retrieval Without Relevance Labels ArXiv, abs/2212.10496. https://doi.org/10.48550/arXiv.2212.10496

Jiang, Z., Yang, M. Y. R., Tsirlin, M., Tang, R., & Lin, J. (2022). Less Is More: Parameter-Free Text Classification With Gzip ArXiv, abs/2212.09410. https://doi.org/10.48550/arXiv.2212.09410

Ma, X., Teofili, T., & Lin, J. (2023). Anserini Gets Dense Retrieval: Integration of Lucene\textquoterights HNSW Indexes ArXiv, abs/2304.12139. https://doi.org/10.48550/arXiv.2304.12139

Pradeep, R., Ma, X., Nogueira, R. F., & Lin, J. (2021). Scientific Claim Verification With VerT5erini Presented at the Scientific Claim Verification With VerT5erini conference. Retrieved from https://www.aclweb.org/anthology/2021.louhi-1.11/

Zhong, Y., Xiao, J., Vetterli, T., Matin, M., Loo, E., Lin, J., … Shapira, O. (2022). Improving Precancerous Case Characterization via Transformer-Based Ensemble Learning Presented at the Mproving Precancerous Case Characterization via Transformer-Based Ensemble Learning conference. Retrieved from https://aclanthology.org/2022.emnlp-industry.38

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

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 Primary Tabs View conference. https://doi.org/10.1145/3572960.3572982

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

Yang, J.-H., Lassance, C., de Rezende, R. S., Srinivasan, K., Redi, M., Clinchant, S. ephane, & Lin, J. (2023). AToMiC: An Image/Text Retrieval Test Collection to Support Multimedia Content Creation ArXiv, abs/2304.01961. https://doi.org/10.48550/arXiv.2304.01961