Andreassen, Anders

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

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 569

Solving Quantitative Reasoning Problems with Language Models

Lewkowycz, A., Andreassen, A., Dohan, D., Dyer, E., Michalewski, H., Ramasesh, V., Slone, A., Anil, C., Schlag, I., Gutman-Solo, T., et al. (2022) · arXiv

Mathematics Education and PedagogyTopic Modeling

Cited by 358

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

Show Your Work: Scratchpads for Intermediate Computation with Language Models

Nye, M., Andreassen, A. J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., et al. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 144

Consistent Use of the Standard Model Effective Potential

Andreassen, A., Frost, W., & Schwartz, M. D. (2014) · Physical Review Letters

Particle physics theoretical and experimental studiesQuantum Chromodynamics and Particle Interactions

Cited by 133

Consistent Use of Effective Potentials

Andreassen, A., Frost, W., & Schwartz, M. D. (2014) · arXiv

Particle physics theoretical and experimental studiesQuantum and Classical Electrodynamics

Cited by 106

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

Understanding the Failure Modes of Out-of-Distribution Generalization

Nagarajan, V., Andreassen, A., & Neyshabur, B. (2020) · arXiv

Domain Adaptation and Few-Shot LearningStochastic Gradient Optimization Techniques

Cited by 55

Exploring Length Generalization in Large Language Models

Anil, C., Wu, Y., Andreassen, A., Lewkowycz, A., Misra, V., Ramasesh, V., Slone, A., Gur-Ari, G., Dyer, E., & Neyshabur, B. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 44