Mehta, Harsh

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

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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

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Transferable Representation Learning in Vision-and-Language Navigation

Huang, H., Jain, V., Mehta, H., Ku, A., Magalhaes, G., Baldridge, J., & Ie, E. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Multimodal Machine Learning ApplicationsReinforcement Learning in Robotics

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Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries

Vodrahalli, K., Ontanon, S., Tripuraneni, N., Xu, K., Jain, S., Shivanna, R., Hui, J., Dikkala, N., Kazemi, M., Fatemi, B., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

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