| A cookbook of self-supervised learning R Balestriero, M Ibrahim, V Sobal, A Morcos, S Shekhar, T Goldstein, ... arXiv preprint arXiv:2304.12210, 2023 | 230* | 2023 |
| Masked siamese networks for label-efficient learning M Assran, M Caron, I Misra, P Bojanowski, F Bordes, P Vincent, A Joulin, ... European Conference on Computer Vision, 456-473, 2022 | 215 | 2022 |
| High fidelity visualization of what your self-supervised representation knows about F Bordes, R Balestriero, P Vincent Transactions of Machine Learning Research (TMLR), 2022 | 45 | 2022 |
| Learning to generate samples from noise through infusion training F Bordes, S Honari, P Vincent ICLR - International Conference on Learning Representations, 2017 | 43* | 2017 |
| Guillotine regularization: Why removing layers is needed to improve generalization in self-supervised learning F Bordes, R Balestriero, Q Garrido, A Bardes, P Vincent Transactions of Machine Learning Research (TMLR), 2022 | 26* | 2022 |
| The hidden uniform cluster prior in self-supervised learning M Assran, R Balestriero, Q Duval, F Bordes, I Misra, P Bojanowski, ... ICLR - International Conference on Learning Representations, 2022 | 25 | 2022 |
| Towards democratizing joint-embedding self-supervised learning F Bordes, R Balestriero, P Vincent arXiv preprint arXiv:2303.01986, 2023 | 15* | 2023 |
| Pug: Photorealistic and semantically controllable synthetic data for representation learning F Bordes, S Shekhar, M Ibrahim, D Bouchacourt, P Vincent, A Morcos Advances in Neural Information Processing Systems 36, 2024 | 14 | 2024 |
| Objectives matter: Understanding the impact of self-supervised objectives on vision transformer representations S Shekhar, F Bordes, P Vincent, A Morcos arXiv preprint arXiv:2304.13089, 2023 | 10 | 2023 |
| Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised Learning C Meehan, F Bordes, P Vincent, K Chaudhuri, C Guo Advances in Neural Information Processing Systems 36, 2024 | 4 | 2024 |
| Iteratively unveiling new regions of interest in deep learning models F Bordes, T Berthier, L Di Jorio, P Vincent, Y Bengio Medical Imaging with Deep Learning, 2018 | 3 | 2018 |
| Evaluation of generative networks through their data augmentation capacity T Lesort, F Bordes, JF Goudou, D Filliat | 2 | 2018 |
| A Picture is Worth More Than 77 Text Tokens: Evaluating CLIP-Style Models on Dense Captions J Urbanek, F Bordes, P Astolfi, M Williamson, V Sharma, ... arXiv preprint arXiv:2312.08578, 2023 | 1 | 2023 |
| Feedback-guided Data Synthesis for Imbalanced Classification R Askari Hemmat, M Pezeshki, F Bordes, M Drozdzal, A Romero-Soriano arXiv e-prints, arXiv: 2310.00158, 2023 | 1* | 2023 |
| A surprisingly simple technique to control the pretraining bias for better transfer: Expand or Narrow your representation F Bordes, S Lavoie, R Balestriero, N Ballas, P Vincent arXiv preprint arXiv:2304.05369, 2023 | 1 | 2023 |
| Predicting masked tokens in stochastic locations improves masked image modeling A Bar, F Bordes, A Shocher, M Assran, P Vincent, N Ballas, T Darrell, ... arXiv preprint arXiv:2308.00566, 2023 | | 2023 |
| Learning to sample from noise with deep generative models F Bordes | | 2017 |
Pascal VincentFacebook AI Research; U. Montreal (Professor, Computer Sc. & Op. Res.); MILA; CIFARVerified email at iro.umontreal.ca
