The llama 3 herd of models A Dubey, A Jauhri, A Pandey, A Kadian, A Al-Dahle, A Letman, A Mathur, ... arXiv preprint arXiv:2407.21783, 2024 | 1356 | 2024 |
The hateful memes challenge: Detecting hate speech in multimodal memes D Kiela, H Firooz, A Mohan, V Goswami, A Singh, P Ringshia, ... Advances in neural information processing systems 33, 2611-2624, 2020 | 614 | 2020 |
Opacus: User-friendly differential privacy library in PyTorch A Yousefpour, I Shilov, A Sablayrolles, D Testuggine, K Prasad, M Malek, ... arXiv preprint arXiv:2109.12298, 2021 | 367 | 2021 |
Supervised multimodal bitransformers for classifying images and text D Kiela, S Bhooshan, H Firooz, E Perez, D Testuggine arXiv preprint arXiv:1909.02950, 2019 | 272 | 2019 |
Llama guard: Llm-based input-output safeguard for human-ai conversations H Inan, K Upasani, J Chi, R Rungta, K Iyer, Y Mao, M Tontchev, Q Hu, ... arXiv preprint arXiv:2312.06674, 2023 | 234 | 2023 |
Are multimodal transformers robust to missing modality? M Ma, J Ren, L Zhao, D Testuggine, X Peng Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 157 | 2022 |
Introducing v0. 5 of the ai safety benchmark from mlcommons B Vidgen, A Agrawal, AM Ahmed, V Akinwande, N Al-Nuaimi, N Alfaraj, ... arXiv preprint arXiv:2404.12241, 2024 | 25 | 2024 |
PyTorch Differential Privacy Series Part 1: DP-SGD Algorithm Explained D Testuggine, I Mironov August, 2020 | 10 | 2020 |