Parisotto, Emilio

Gemma 3 Technical Report

Gemma Team, Kamath, A., Ferret, J., Pathak, S., Vieillard, N., Merhej, R., Perrin, S., Matejovicova, T., Ramé, A., Rivière, M., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 1,156

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

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

Stabilizing Transformers for Reinforcement Learning

Parisotto, E., Song, H. F., Rae, J. W., Pascanu, R., Gulcehre, C., Jayakumar, S. M., Jaderberg, M., Kaufman, R. L., Clark, A., Noury, S., et al. (2019) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 199

Neural Map: Structured Memory for Deep Reinforcement Learning

Parisotto, E., & Salakhutdinov, R. (2017) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 178

Global Pose Estimation with an Attention-Based Recurrent Network

Parisotto, E., Chaplot, D. S., Zhang, J., & Salakhutdinov, R. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Robotics and Sensor-Based LocalizationReinforcement Learning in Robotics

Cited by 88

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

A Generalist Agent

Reed, S., Zolna, K., Parisotto, E., Colmenarejo, S. G., Novikov, A., Barth-Maron, G., Gimenez, M., Sulsky, Y., Kay, J., Springenberg, J. T., et al. (2022) · arXiv

Reinforcement Learning in RoboticsMultimodal Machine Learning Applications

Cited by 63

In-context Reinforcement Learning with Algorithm Distillation

Laskin, M., Wang, L., Oh, J., Parisotto, E., Spencer, S., Steigerwald, R., Strouse, D., Hansen, S., Filos, A., Brooks, E., et al. (2022) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 14

RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation

Bousmalis, K., Vezzani, G., Rao, D., Devin, C., Lee, A. X., Bauza, M., Davchev, T., Zhou, Y., Gupta, A., Raju, A., et al. (2023) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 13

Gemini Robotics: Bringing AI into the Physical World

Gemini Robotics Team, Abeyruwan, S., Ainslie, J., Alayrac, J.-B., Arenas, M. G., Armstrong, T., Balakrishna, A., Baruch, R., Bauza, M., Blokzijl, M., et al. (2025) · arXiv

Robot Manipulation and LearningMultimodal Machine Learning Applications

Cited by 8