Peng, Daiyi

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

DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection

Li, Y., Yu, A. W., Meng, T., Caine, B., Ngiam, J., Peng, D., Shen, J., Lu, Y., Zhou, D., Le, Q. V., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Autonomous Vehicle Technology and SafetyAdvanced Neural Network Applications

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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

Long-form factuality in large language models

Wei, J., Yang, C., Song, X., Lu, Y., Hu, N., Huang, J., Tran, D., Peng, D., Liu, R., Huang, D., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

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Best Practices and Lessons Learned on Synthetic Data

Liu, R., Wei, J., Liu, F., Si, C., Zhang, Y., Rao, J., Zheng, S., Peng, D., Yang, D., Zhou, D., et al. (2024) · arXiv

Topic ModelingEthics and Social Impacts of AI

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SEVerA: Verified Synthesis of Self-Evolving Agents

Banerjee, D., Xu, C., Ie, E., Zhang, M., Peng, D., Lin, C.-C., Suresh, T., & Singh, G. (2026) · arXiv

Logic, programming, and type systemsFormal Methods in Verification

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