Ainslie, Joshua

Big Bird: Transformers for Longer Sequences

Zaheer, M., Guruganesh, G., Dubey, A., Ainslie, J., Alberti, C., Ontanon, S., Pham, P., Ravula, A., Wang, Q., Yang, L., et al. (2020) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1,349

Gemini: A Family of Highly Capable Multimodal Models

Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 990

GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints

Ainslie, J., Lee-Thorp, J., de Jong, M., Zemlyanskiy, Y., Lebron, F., & Sanghai, S. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 454

GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints

Ainslie, J., Lee-Thorp, J., de Jong, M., Zemlyanskiy, Y., Lebrón, F., & Sanghai, S. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 447

LongT5: Efficient Text-To-Text Transformer for Long Sequences

Guo, M., Ainslie, J., Uthus, D., Ontanon, S., Ni, J., Sung, Y.-H., & Yang, Y. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 230

LongT5: Efficient Text-To-Text Transformer for Long Sequences

Guo, M., Ainslie, J., Uthus, D., Ontanon, S., Ni, J., Sung, Y.-H., & Yang, Y. (2022) · Findings of the Association for Computational Linguistics: NAACL 2022

Topic ModelingNatural Language Processing Techniques

Cited by 230

FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction

Lee, C.-Y., Li, C.-L., Dozat, T., Perot, V., Su, G., Hua, N., Ainslie, J., Wang, R., Fujii, Y., & Pfister, T. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Handwritten Text Recognition TechniquesTopic Modeling

Cited by 74

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

Generate-and-Retrieve: use your predictions to improve retrieval for semantic parsing

Zemlyanskiy, Y., de Jong, M., Ainslie, J., Pasupat, P., Shaw, P., Qiu, L., Sanghai, S., & Sha, F. (2022) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 8

Gemini Robotics: Bringing AI into the Physical World

Gemini Robotics Team, Abeyruwan, S., Ainslie, J., Alayrac, J.-B., Arenas, M. G., Armstrong, T., Balakrishna, A., Baruch, R., Bauza, M., Blokzijl, M., et al. (2025) · arXiv

Robot Manipulation and LearningMultimodal Machine Learning Applications

Cited by 8