Rao, Jinfeng

Long Range Arena: A Benchmark for Efficient Transformers

Tay, Y., Dehghani, M., Abnar, S., Shen, Y., Bahri, D., Pham, P., Rao, J., Yang, L., Ruder, S., & Metzler, D. (2020) · arXiv

Topic ModelingAdvanced Neural Network Applications

Cited by 241

Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue

Balakrishnan, A., Rao, J., Upasani, K., White, M., & Subba, R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Speech and dialogue systemsNatural Language Processing Techniques

Cited by 70

Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers

Tay, Y., Dehghani, M., Rao, J., Fedus, W., Abnar, S., Chung, H. W., Narang, S., Yogatama, D., Vaswani, A., & Metzler, D. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 63

Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?

Tay, Y., Dehghani, M., Abnar, S., Chung, H., Fedus, W., Rao, J., Narang, S., Tran, V., Yogatama, D., & Metzler, D. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023

Topic ModelingNatural Language Processing Techniques

Cited by 34

DSI++: Updating Transformer Memory with New Documents

Mehta, S., Gupta, J., Tay, Y., Dehghani, M., Tran, V., Rao, J., Najork, M., Strubell, E., & Metzler, D. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingInformation Retrieval and Search Behavior

Cited by 21

Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?

Tay, Y., Dehghani, M., Abnar, S., Chung, H. W., Fedus, W., Rao, J., Narang, S., Tran, V. Q., Yogatama, D., & Metzler, D. (2022) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 11