Sheahan, Hannah

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

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini Team, Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 306

Structure learning and the posterior parietal cortex

Summerfield, C., Luyckx, F., & Sheahan, H. (2020) · Progress in Neurobiology

Visual perception and processing mechanismsMemory and Neural Mechanisms

Cited by 118

Language models, like humans, show content effects on reasoning tasks

Lampinen, A. K., Dasgupta, I., Chan, S. C. Y., Sheahan, H. R., Creswell, A., Kumaran, D., McClelland, J. L., & Hill, F. (2024) · PNAS Nexus

Logic, Reasoning, and KnowledgeTopic Modeling

Cited by 69