Open Access
| Issue |
E3S Web Conf.
Volume 460, 2023
International Scientific Conference on Biotechnology and Food Technology (BFT-2023)
|
|
|---|---|---|
| Article Number | 04027 | |
| Number of page(s) | 6 | |
| Section | IoT, Big Data and AI in Food Industry | |
| DOI | https://doi.org/10.1051/e3sconf/202346004027 | |
| Published online | 11 December 2023 | |
- B. Lester, R. Al-Rfou, N. Constant, The power of scale for parameter-efficient prompt tuning. In EMNLP (2021) [Google Scholar]
- A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, I. Polosukhin, Attention is all you need. In NeurIPS (2017) [Google Scholar]
- Y. Wu, M. Schuster, Z. Chen, Q. V. Le, M. Norouzi, W. Macherey, M. Krikun, Y. Cao, Q. Gao, K. Macherey, et al., Google’s neural machine translation system: Bridging the gap be- tween human and machine translation, arXiv preprint arXiv, 1609, 08144 (2016) [Google Scholar]
- P. Pylov, R. Maitak, A. Dyagileva, A. Protodyakonov, Development of the architecture of a transformer-based neural network model to automate delivering judgments in bankruptcy cases E3S Web of Conferences, 402, 03034 (2023) [CrossRef] [EDP Sciences] [Google Scholar]
- Q. Xu, A. Baevski, T. Likhomanenko, P. Tomasello, A. Conneau, R. Collobert, G. Synnaeve, M. Auli, Self-training and pre-training are complementary for speech recognition. In ICASSP (2021) [Google Scholar]
- R. Sanabria, O. Caglayan, S. Palaskar, D. Elliott, L. Barrault, L. Specia, F. Metze, How2: a large-scale dataset for multimodal language un- derstanding, Proceedings of the Workshop on Visually Grounded Interaction and Language (ViGIL) (2018) [Google Scholar]
- D. Serdyuk, O. Braga, O. Siohan, Audio-visual speech recognition is worth 32x32x8 voxels, 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) (2021) [Google Scholar]
- Yi-Lin Sung, Jaemin Cho, Mohit Bansal, Vl-adapter: Parameter-efficient transfer learning for vision and language tasks, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 5227–5237 (2022) [Google Scholar]
- D. Serdyuk, O. Braga, O. Siohan, Audio-visual speech recognition is worth 32x32x8 voxels, 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) (2021) [Google Scholar]
- Yi-Lin Sung, Jaemin Cho, Mohit Bansal, Vl-adapter: Parameter-efficient transfer learning for vision and language tasks, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 5227–5237 (2022) [Google Scholar]
- Tejas Srinivasan, Ramon Sanabria, Florian Metze, Looking enhances listening: Recovering missing speech using im- ages, ICASSP (2020) [Google Scholar]
- G. Synnaeve, Q. Xu, J. Kahn, T. Likhomanenko, E. Grave, V. Pratap, Anuroop Sriram, Vitaliy Liptchinsky, and Ronan Collobert. End-to-end asr: from supervised to semi-supervised learning with modern architectures, arXiv preprint arXiv, 1911, 08460 (2019) [Google Scholar]
- I. Loshchilov, F. Hutter, Decoupled weight decay regularization, International Conference on Learning Representations (2019) [Google Scholar]
- Abhinav Gupta, Yajie Miao, Leonardo Neves, Florian Metze, Visual features for context-aware speech recognition, ICASSP (2017) [Google Scholar]
- Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, Sylvain Gelly, Parameter-efficient transfer learning for nlp. International Conference on Machine Learning, PMLR, 2790–2799 (2019) [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.