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
References
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2021
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
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
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
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
Lin, J. (2021). A Proposed Conceptual Framework for a Representational Approach To Information Retrieval SIGIR Forum, 55, 1-4. https://doi.org/10.1145/3527546.3527552
Toman, D., & Wedell, G. (2021). Projective Beth Definability and Craig Interpolation for Relational Query Optimization (Material to Accompany Invited Talk) Presented at the Projective Beth Definability and Craig Interpolation for Relational Query Optimization (Material to Accompany Invited Talk) conference. Retrieved from http://ceur-ws.org/Vol-3009/invited1.pdf
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
Li, H., Zhuang, S., Mourad, A., Ma, X., Lin, J., & Zuccon, G. (2021). Improving Query Representations for Dense Retrieval With Pseudo Relevance Feedback: A Reproducibility Study ArXiv, abs/2112.06400. Retrieved from https://arxiv.org/abs/2112.06400
Craswell, N., Mitra, B., Yilmaz, E., Campos, D., & Lin, J. (2021). Overview of the TREC 2021 Deep Learning Track Presented at the Overview of the TREC 2021 Deep Learning Track conference. Retrieved from https://trec.nist.gov/pubs/trec30/papers/Overview-DL.pdf