Swersky, Kevin

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

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

Video models are zero-shot learners and reasoners

Wiedemer, T., Li, Y., Vicol, P., Gu, S. S., Matarese, N., Swersky, K., Kim, B., Jaini, P., & Geirhos, R. (2025) · arXiv

Multimodal Machine Learning ApplicationsGenerative Adversarial Networks and Image Synthesis

Cited by 2

Enhancing LLM Planning Capabilities through Intrinsic Self-Critique

Bohnet, B., Kamienny, P.-A., Sedghi, H., Gorur, D., Awasthi, P., Parisi, A., Swersky, K., Liu, R., Nova, A., & Fiedel, N. (2025) · arXiv

AI-based Problem Solving and PlanningArtificial Intelligence in Games

Cited by 0