Guadarrama, Sergio

Caffe: Convolutional Architecture for Fast Feature Embedding

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia

Advanced Neural Network ApplicationsParallel Computing and Optimization Techniques

Cited by 11,077

Long-term recurrent convolutional networks for visual recognition and description

Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Darrell, T., & Saenko, K. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsMultimodal Machine Learning Applications

Cited by 5,228

Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors

Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., Guadarrama, S., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 2,743

Long-Term Recurrent Convolutional Networks for Visual Recognition and Description

Donahue, J., Hendricks, L. A., Rohrbach, M., Venugopalan, S., Guadarrama, S., Saenko, K., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 1,547

Improved Image Captioning via Policy Gradient optimization of SPIDEr

Liu, S., Zhu, Z., Ye, N., Guadarrama, S., & Murphy, K. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV)

Multimodal Machine Learning ApplicationsGenerative Adversarial Networks and Image Synthesis

Cited by 430

LSDA: Large Scale Detection Through Adaptation

Hoffman, J., Guadarrama, S., Tzeng, E., Hu, R., Donahue, J., Girshick, R., Darrell, T., & Saenko, K. (2014) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 284

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

Measuring the Reliability of Reinforcement Learning Algorithms

Chan, S. C. Y., Fishman, S., Canny, J., Korattikara, A., & Guadarrama, S. (2019) · arXiv

Reinforcement Learning in RoboticsMachine Learning and Algorithms

Cited by 57

Multi-Game Decision Transformers

Lee, K.-H., Nachum, O., Yang, M. (., Lee, L., Freeman, D., Guadarrama, S., Fischer, I., Xu, W., Jang, E., Michalewski, H., et al. (2022) · Advances in Neural Information Processing Systems 35

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 54