Huang, C., Xie, Y., Jiang, Z., Lin, J., & Li, M. (2023). Approximating Human-Like Few-Shot Learning With GPT-based Compression ArXiv, abs/2308.06942. https://doi.org/10.48550/arXiv.2308.06942
References
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2023
Zou, L., Pang, Y., Ozsu, T., & Chen, J. (2023). Efficient Execution of SPARQL Queries With OPTIONAL and UNION Expressions ArXiv, abs/2303.13844. https://doi.org/10.48550/arXiv.2303.13844
Ehrlinger, L., Harmouch, H., Ilyas, I., & Naumann, F. (2023). Preface QDB Presented at the Paper Preface QDB conference. Retrieved from https://ceur-ws.org/Vol-3462/QDB0.pdf
Hebert, L., Golab, L., Poupart, P., & Cohen, R. (2023). FedFormer: Contextual Federation With Attention in Reinforcement Learning Presented at the FedFormer: Contextual Federation With Attention in Reinforcement Learning conference. https://doi.org/10.5555/3545946.3598716
Mohapatra, S., Zong, J., Kerschbaum, F., & He, X. (2023). Differentially Private Data Generation With Missing Data ArXiv, abs/2310.11548. https://doi.org/10.48550/ARXIV.2310.11548
Ren, H., Mousavi, A., Pacaci, A., Chowdhury, S. R., Mohoney, J., Ilyas, I., … Rekatsinas, T. (2023). Fact Ranking Over Large-Scale Knowledge Graphs With Reasoning Embedding Models IEEE Data Engineering Bulletin, 46, 126-139. Retrieved from http://sites.computer.org/debull/A23june/p126.pdf
Tang, R., Zhang, X., Ma, X., Lin, J., & Türe, F. (2023). Found in the Middle: Permutation Self-Consistency Improves Listwise Ranking in Large Language Models ArXiv, abs/2310.07712. https://doi.org/10.48550/ARXIV.2310.07712
Zhong, W., Xie, Y., & Lin, J. (2023). Answer Retrieval for Math Questions Using Structural and Dense Retrieval Presented at the Retrieval for Math Questions Using Structural and Dense Retrieval conference. https://doi.org/10.1007/978-3-031-42448-9_18
Zhang, C., Bonifati, A., & Ozsu, T. (2023). Indexing Techniques for Graph Reachability Queries ArXiv, abs/2311.03542. https://doi.org/10.48550/ARXIV.2311.03542
Lin, S.-C., Asai, A., Li, M., Oguz, B., Lin, J., Mehdad, Y., … Chen, X. (2023). How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval ArXiv, abs/2302.07452. https://doi.org/10.48550/arXiv.2302.07452