Eslami, S. M. Ali

The Pascal Visual Object Classes Challenge: A Retrospective

Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015) · International Journal of Computer Vision

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 7,139

Data-Efficient Image Recognition with Contrastive Predictive Coding

Hénaff, O. J., Srinivas, A., De Fauw, J., Razavi, A., Doersch, C., Eslami, S. M. A., & Oord, A. V. D. (2019) · arXiv

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 1,014

Emergence of Locomotion Behaviours in Rich Environments

Heess, N., TB, D., Sriram, S., Lemmon, J., Merel, J., Wayne, G., Tassa, Y., Erez, T., Wang, Z., Eslami, S. M. A., et al. (2017) · arXiv

Reinforcement Learning in RoboticsRobotic Locomotion and Control

Cited by 657

A probabilistic u-net for segmentation of ambiguous images

Kohl, S. A. A., Romera-Paredes, B., Meyer, C., De Fauw, J., Ledsam, J. R., Maier-Hein, K. H., Eslami, S. M. A., Rezende, D. J., & Ronneberger, O. (2018) · arXiv

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 439

Multimodal Few-Shot Learning with Frozen Language Models

Tsimpoukelli, M., Menick, J., Cabi, S., Eslami, S. M. A., Vinyals, O., & Hill, F. (2021) · arXiv

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 355

Conditional Neural Processes

Garnelo, M., Rosenbaum, D., Maddison, C. J., Ramalho, T., Saxton, D., Shanahan, M., Teh, Y. W., Rezende, D. J., & Eslami, S. M. A. (2018) · arXiv

Gaussian Processes and Bayesian InferenceNeural Networks and Applications

Cited by 287

Collaboration between clinicians and vision–language models in radiology report generation

Tanno, R., Barrett, D. G. T., Sellergren, A., Ghaisas, S., Dathathri, S., See, A., Welbl, J., Lau, C., Tu, T., Azizi, S., et al. (2025) · Nature Medicine

Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications

Cited by 186

Capabilities of Gemini Models in Medicine

Saab, K., Tu, T., Weng, W.-H., Tanno, R., Stutz, D., Wulczyn, E., Zhang, F., Strother, T., Park, C., Vedadi, E., et al. (2024) · arXiv

Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications

Cited by 117

From motor control to team play in simulated humanoid football

Liu, S., Lever, G., Wang, Z., Merel, J., Eslami, S. M. A., Hennes, D., Czarnecki, W. M., Tassa, Y., Omidshafiei, S., Abdolmaleki, A., et al. (2022) · Science Robotics

Reinforcement Learning in RoboticsHuman Motion and Animation

Cited by 88

Kickstarting Deep Reinforcement Learning

Schmitt, S., Hudson, J. J., Zidek, A., Osindero, S., Doersch, C., Czarnecki, W. M., Leibo, J. Z., Kuttler, H., Zisserman, A., Simonyan, K., et al. (2018) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 67

The Shape Boltzmann Machine: A strong model of object shape

Eslami, S. M. A., Heess, N., & Winn, J. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition

Advanced Neural Network ApplicationsNeural Networks and Applications

Cited by 61

Advancing Multimodal Medical Capabilities of Gemini

Yang, L., Xu, S., Sellergren, A., Kohlberger, T., Zhou, Y., Ktena, I., Kiraly, A., Ahmed, F., Hormozdiari, F., Jaroensri, T., et al. (2024) · arXiv

Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications

Cited by 34