Rabbat, Michael

DINOv2: Learning Robust Visual Features without Supervision

Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., et al. (2023) · arXiv

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 1,078

Distributed optimization in sensor networks

Rabbat, M., & Nowak, R. (2004) · Proceedings of the 3rd international symposium on Information processing in sensor networks

Distributed Sensor Networks and Detection AlgorithmsDistributed Control Multi-Agent Systems

Cited by 901

Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture

Assran, M., Duval, Q., Misra, I., Bojanowski, P., Vincent, P., Rabbat, M., LeCun, Y., & Ballas, N. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 456

Push-Sum Distributed Dual Averaging for convex optimization

Tsianos, K. I., Lawlor, S., & Rabbat, M. G. (2012) · 2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

Distributed Control Multi-Agent SystemsStochastic Gradient Optimization Techniques

Cited by 304

Distributed consensus and optimization under communication delays

Tsianos, K. I., & Rabbat, M. G. (2011) · 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

Distributed Control Multi-Agent SystemsStability and Control of Uncertain Systems

Cited by 92

SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum

Wang, J., Tantia, V., Ballas, N., & Rabbat, M. (2019) · arXiv

Stochastic Gradient Optimization TechniquesDistributed Control Multi-Agent Systems

Cited by 90

A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale

Shi, H.-J. M., Lee, T.-H., Iwasaki, S., Gallego-Posada, J., Li, Z., Rangadurai, K., Mudigere, D., & Rabbat, M. (2023) · arXiv

Stochastic Gradient Optimization TechniquesParallel Computing and Optimization Techniques

Cited by 70

The Hidden Uniform Cluster Prior in Self-Supervised Learning

Assran, M., Balestriero, R., Duval, Q., Bordes, F., Misra, I., Bojanowski, P., Vincent, P., Rabbat, M., & Ballas, N. (2022) · arXiv

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 18

Revisiting Feature Prediction for Learning Visual Representations from Video

Bardes, A., Garrido, Q., Ponce, J., Chen, X., Rabbat, M., LeCun, Y., Assran, M., & Ballas, N. (2024) · arXiv

Domain Adaptation and Few-Shot LearningHuman Pose and Action Recognition

Cited by 18

V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

Assran, M., Bardes, A., Fan, D., Garrido, Q., Howes, R., Mojtaba, Komeili, Muckley, M., Rizvi, A., Roberts, C., et al. (2025) · arXiv

Human Pose and Action RecognitionMultimodal Machine Learning Applications

Cited by 8

Intuitive physics understanding emerges from self-supervised pretraining on natural videos

Garrido, Q., Ballas, N., Assran, M., Bardes, A., Najman, L., Rabbat, M., Dupoux, E., & LeCun, Y. (2025) · arXiv

Child and Animal Learning DevelopmentMultimodal Machine Learning Applications

Cited by 3