| 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 | 2413 | 2024 |
| Code llama: Open foundation models for code B Roziere, J Gehring, F Gloeckle, S Sootla, I Gat, XE Tan, Y Adi, J Liu, ... arXiv preprint arXiv:2308.12950, 2023 | 1661 | 2023 |
| Simple and controllable music generation J Copet, F Kreuk, I Gat, T Remez, D Kant, G Synnaeve, Y Adi, A Défossez Advances in Neural Information Processing Systems 36, 2024 | 463 | 2024 |
| Deep functional maps: Structured prediction for dense shape correspondence O Litany, T Remez, E Rodola, A Bronstein, M Bronstein Proceedings of the IEEE international conference on computer vision, 5659-5667, 2017 | 334 | 2017 |
| Learning to segment via cut-and-paste T Remez, J Huang, M Brown Proceedings of the European conference on computer vision (ECCV), 37-52, 2018 | 121 | 2018 |
| Class-aware fully convolutional Gaussian and Poisson denoising T Remez, O Litany, R Giryes, AM Bronstein IEEE Transactions on Image Processing 27 (11), 5707-5722, 2018 | 99 | 2018 |
| Efficient deformable shape correspondence via kernel matching M Vestner, Z Lähner, A Boyarski, O Litany, R Slossberg, T Remez, ... 2017 international conference on 3D vision (3DV), 517-526, 2017 | 96 | 2017 |
| System and methods for risk management analysis of a pressure sensing system AB Shalom, S Hanassy, L Greenstein, T Remez US Patent App. 14/372,030, 2014 | 96 | 2014 |
| Deep convolutional denoising of low-light images T Remez, O Litany, R Giryes, AM Bronstein arXiv preprint arXiv:1701.01687, 2017 | 83 | 2017 |
| Into the wild with audioscope: Unsupervised audio-visual separation of on-screen sounds E Tzinis, S Wisdom, A Jansen, S Hershey, T Remez, DPW Ellis, ... arXiv preprint arXiv:2011.01143, 2020 | 78 | 2020 |
| Pressure sensor assembly and associated method for preventing the development of pressure injuries D Weiss, R Poliakine-Baruchi, AB Shalom, L Greenstein, Y Assouline, ... US Patent App. 13/881,169, 2013 | 78 | 2013 |
| The llama 3 herd of models A Grattafiori, A Dubey, A Jauhri, A Pandey, A Kadian, A Al-Dahle, ... arXiv e-prints, arXiv: 2407.21783, 2024 | 77 | 2024 |
| Translatotron 2: High-quality direct speech-to-speech translation with voice preservation Y Jia, MT Ramanovich, T Remez, R Pomerantz International Conference on Machine Learning, 10120-10134, 2022 | 73 | 2022 |
| Deep class-aware image denoising T Remez, O Litany, R Giryes, AM Bronstein 2017 international conference on sampling theory and applications (SampTA …, 2017 | 73 | 2017 |
| Textually pretrained speech language models M Hassid, T Remez, TA Nguyen, I Gat, A Conneau, F Kreuk, J Copet, ... Advances in Neural Information Processing Systems 36, 63483-63501, 2023 | 59 | 2023 |
| Code llama: Open foundation models for code.(2023) B Rozière, J Gehring, F Gloeckle, S Sootla, I Gat, XE Tan, Y Adi, J Liu, ... arXiv preprint arXiv:2308.12950, 2023 | 56 | 2023 |
| Expresso: A benchmark and analysis of discrete expressive speech resynthesis TA Nguyen, WN Hsu, A d'Avirro, B Shi, I Gat, M Fazel-Zarani, T Remez, ... arXiv preprint arXiv:2308.05725, 2023 | 50 | 2023 |
| Audioscopev2: Audio-visual attention architectures for calibrated open-domain on-screen sound separation E Tzinis, S Wisdom, T Remez, JR Hershey European Conference on Computer Vision, 368-385, 2022 | 34 | 2022 |
| Masked audio generation using a single non-autoregressive transformer A Ziv, I Gat, GL Lan, T Remez, F Kreuk, A Défossez, J Copet, G Synnaeve, ... arXiv preprint arXiv:2401.04577, 2024 | 32 | 2024 |
| Revise: Self-supervised speech resynthesis with visual input for universal and generalized speech regeneration WN Hsu, T Remez, B Shi, J Donley, Y Adi Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 31* | 2023 |
Or LitanyTechnion, NVIDIAEmail verificata su nvidia.com