Schucher, Nathan

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

PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models

Scholak, T., Schucher, N., & Bahdanau, D. (2021) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 346

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

DECoVaC: Design of Experiments with Controlled Variability Components

Boquet, T., Delisle, L., Kochetkov, D., Schucher, N., Atighehchian, P., Oreshkin, B., & Cornebise, J. (2019) · arXiv

Machine Learning and Data ClassificationOptimal Experimental Design Methods

Cited by 1