Lin, J., Li, J., Gao, J., Ma, W., & Liu, Y. (2024). Jointly Modeling Spatio-Temporal Features of Tactile Signals for Action Classification Presented at the Modeling Spatio-Temporal Features of Tactile Signals for Action Classification conference. https://doi.org/10.1609/AAAI.V38I12.29288
References
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Khalaji, M., Brown, T., Daudjee, K., & Aksenov, V. (2024). Practical Hardware Transactional vEB Trees Presented at the Practical Hardware Transactional VEB Trees conference. https://doi.org/10.1145/3627535.3638504
Arabzadeh, N., Bigdeli, A., & Clarke, C. (2024). Adapting Standard Retrieval Benchmarks to Evaluate Generated Answers ArXiv, abs/2401.04842. https://doi.org/10.48550/ARXIV.2401.04842
He, X. (2024). Technical Perspective: Synthetic Data Needs a Reproducibility Benchmark SIGMOD Record, 53, 64. https://doi.org/10.1145/3665252.3665266
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
Clarke, C., Diaz, F., & Arabzadeh, N. (2023). Preference-Based Offline Evaluation Presented at the Preference-Based Offline Evaluation conference. https://doi.org/10.1145/3539597.3572725
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
Ilyas, I., Lacerda, J. P., Li, Y., Minhas, U. F., Mousavi, A., Pound, J., … Sumanth, C. (2023). Growing and Serving Large Open-Domain Knowledge Graphs ArXiv, abs/2305.09464. https://doi.org/10.48550/arXiv.2305.09464
Tamber, M. S., Pradeep, R., & Lin, J. (2023). Scaling Down, LiTting Up: Efficient Zero-Shot Listwise Reranking With Seq2seq Encoder-Decoder Models ArXiv, abs/2312.16098. https://doi.org/10.48550/ARXIV.2312.16098
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