Gao, L., Ma, X., Lin, J., & Callan, J. (2022). Tevatron: An Efficient and Flexible Toolkit for Dense Retrieval ArXiv, abs/2203.05765. https://doi.org/10.48550/arXiv.2203.05765
References
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2022
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
2021
Lin, S.-C., Yang, J.-H., Nogueira, R., Tsai, M.-F., Wang, C.-J., & Lin, J. (2021). Multi-Stage Conversational Passage Retrieval: An Approach to Fusing Term Importance Estimation and Neural Query Rewriting ACM Transactions on Information Systems (TOIS), 39, 1-48. https://doi.org/10.1145/3446426
Mackenzie, J., Trotman, A., & Lin, J. (2021). Wacky Weights in Learned Sparse Representations and the Revenge Of Score-at-a-Time Query Evaluation ArXiv, abs/2110.11540. Retrieved from https://arxiv.org/abs/2110.11540
Brown, D. G., Byl, L., & Grossman, M. (2021). Are Machine Learning Corpora "Fair Dealing" Under Canadian Law? Presented at the Are Machine Learning Corpora "Fair Dealing" Under Canadian Law? conference. Retrieved from https://computationalcreativity.net/iccc21/wp-content/uploads/2021/09/ICCC_2021_paper_68.pdf
Sakr, S., Bonifati, A., Voigt, H., Iosup, A., Ammar, K., Angles, R., … Yoneki, E. (2021). The Future Is Big Graphs: A Community View on Graph Processing Systems Communications of the ACM, 64, 62-71. https://doi.org/10.1145/3434642
Li, M., Li, M., Xiong, K., & Lin, J. (2021). Multi-Task Dense Retrieval via Model Uncertainty Fusion for Open-Domain Question Answering Presented at the Multi-Task Dense Retrieval via Model Uncertainty Fusion for Open-Domain Question Answering conference. Retrieved from https://aclanthology.org/2021.findings-emnlp.26
Near, J. P., & He, X. (2021). Differential Privacy for Databases Foundations and Trends in Databases, 11, 109-225. https://doi.org/10.1561/1900000066
Lin, S.-C., Yang, J.-H., & Lin, J. (2021). In-Batch Negatives for Knowledge Distillation With Tightly-Coupled Teachers for Dense Retrieval Presented at the In-Batch Negatives for Knowledge Distillation With Tightly-Coupled Teachers for Dense Retrieval conference. Retrieved from https://aclanthology.org/2021.repl4nlp-1.17
Liu, J., Knopf, K., Tan, Y., Ding, B., & He, X. (2021). Catch a Blowfish Alive: A Demonstration of Policy-Aware Differential Privacy for Interactive Data Exploration Proceedings of the VLDB Endowment (PVLDB), 14, 2859-2862. Retrieved from http://www.vldb.org/pvldb/vol14/p2859-liu.pdf