Ballas, Nicolas

FitNets: Hints for Thin Deep Nets

Romero, A., Ballas, N., Kahou, S. E., Chassang, A., Gatta, C., & Bengio, Y. (2014) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 2,859

A Closer Look at Memorization in Deep Networks

Arpit, D., Jastrzębski, S., Ballas, N., Krueger, D., Bengio, E., Kanwal, M. S., Maharaj, T., Fischer, A., Courville, A., Bengio, Y., et al. (2017) · arXiv

Stochastic Gradient Optimization TechniquesNeural Networks and Applications

Cited by 1,307

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

Describing Videos by Exploiting Temporal Structure

Yao, L., Torabi, A., Cho, K., Ballas, N., Pal, C., Larochelle, H., & Courville, A. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV)

Multimodal Machine Learning ApplicationsHuman Pose and Action Recognition

Cited by 1,035

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

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 Dissection of Overfitting and Generalization in Continuous Reinforcement Learning

Zhang, A., Ballas, N., & Pineau, J. (2018) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 88

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

A Shortcut-aware Video-QA Benchmark for Physical Understanding via Minimal Video Pairs

Krojer, B., Komeili, M., Ross, C., Garrido, Q., Sinha, K., Ballas, N., & Assran, M. (2025) · arXiv

Multimodal Machine Learning ApplicationsHuman Pose and Action Recognition

Cited by 0