Nematzadeh, Aida

Flamingo: a Visual Language Model for Few-Shot Learning

Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al. (2022) · arXiv

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 1,555

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Rae, J. W., Borgeaud, S., Cai, T., Millican, K., Hoffmann, J., Song, F., Aslanides, J., Henderson, S., Ring, R., Young, S., et al. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 235

Decoupling the Role of Data, Attention, and Losses in Multimodal Transformers

Hendricks, L. A., Mellor, J., Schneider, R., Alayrac, J.-B., & Nematzadeh, A. (2021) · Transactions of the Association for Computational Linguistics

Multimodal Machine Learning ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 100

Probing Image-Language Transformers for Verb Understanding

Hendricks, L. A., & Nematzadeh, A. (2021) · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 63

Evaluating Theory of Mind in Question Answering

Nematzadeh, A., Burns, K., Grant, E., Gopnik, A., & Griffiths, T. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Topic ModelingChild and Animal Learning Development

Cited by 53

How FaR Are Large Language Models From Agents with Theory-of-Mind?

Zhou, P., Madaan, A., Potharaju, S. P., Gupta, A., McKee, K. R., Holtzman, A., Pujara, J., Ren, X., Mishra, S., Nematzadeh, A., et al. (2023) · arXiv

Topic ModelingChild and Animal Learning Development

Cited by 6