Culp, Laura

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

Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging

Azizi, S., Culp, L., Freyberg, J., Mustafa, B., Baur, S., Kornblith, S., Chen, T., Tomasev, N., Mitrović, J., Strachan, P., et al. (2023) · Nature Biomedical Engineering

Domain Adaptation and Few-Shot LearningArtificial Intelligence in Healthcare and Education

Cited by 250

Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach

Jurenka, I., Kunesch, M., McKee, K. R., Gillick, D., Zhu, S., Wiltberger, S., Phal, S. M., Hermann, K., Kasenberg, D., Bhoopchand, A., et al. (2024) · arXiv

Artificial Intelligence in EducationIntelligent Tutoring Systems and Adaptive Learning

Cited by 24

Do generative video models understand physical principles?

Motamed, S., Culp, L., Swersky, K., Jaini, P., & Geirhos, R. (2025) · arXiv

Generative Adversarial Networks and Image SynthesisMultimodal Machine Learning Applications

Cited by 19

Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models

Singh, A., Co-Reyes, J. D., Agarwal, R., Anand, A., Patil, P., Garcia, X., Liu, P. J., Harrison, J., Lee, J., Xu, K., et al. (2023) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 10